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1

Deep Learning based Recommender System: A Survey and New

Perspectives

SHUAI ZHANG, University of New South Wales

LINA YAO, University of New South Wales

AIXIN SUN, Nanyang Technological University

YI TAY, Nanyang Technological University

With the ever-growing volume of online information, recommender systems have been an eective strategy to overcome

such information overload. e utility of recommender systems cannot be overstated, given its widespread adoption in many

web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep

learning has garnered considerable interest in many research elds such as computer vision and natural language processing,

owing not only to stellar performance but also the aractive property of learning feature representations from scratch. e

inuence of deep learning is also pervasive, recently demonstrating its eectiveness when applied to information retrieval and

recommender systems research. Evidently, the eld of deep learning in recommender system is ourishing. is article aims

to provide a comprehensive review of recent research eorts on deep learning based recommender systems. More concretely,

we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive

summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new

exciting development of the eld.

CCS Concepts: •Information systems →Recommender systems;

Additional Key Words and Phrases: Recommender System; Deep Learning; Survey

ACM Reference format:

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2018. Deep Learning based Recommender System: A Survey and New

Perspectives. ACM Comput. Surv. 1, 1, Article 1 (July 2018), 35 pages.

DOI: 0000001.0000001

1 INTRODUCTION

Recommender systems are an intuitive line of defense against consumer over-choice. Given the explosive growth

of information available on the web, users are oen greeted with more than countless products, movies or

restaurants. As such, personalization is an essential strategy for facilitating a beer user experience. All in all,

these systems have been playing a vital and indispensable role in various information access systems to boost

business and facilitate decision-making process [

69

,

121

] and are pervasive across numerous web domains such

as e-commerce and/or media websites.

Yi Tay is added as an author later to help revise the paper for the major revision.

Author’s addresses: S. Zhang and L. Yao, University of New South Wales; emails: shuai.zhang@unsw.edu.au; lina.yao@unsw.edu.au; A. Sun

and Y. Tay, Nanyang Technological University; email: axsun@ntu.edu.sg; ytay017@e.ntu.edu.sg;

ACM acknowledges that this contribution was authored or co-authored by an employee, or contractor of the national government. As such,

the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government

purposes only. Permission to make digital or hard copies for personal or classroom use is granted. Copies must bear this notice and the

full citation on the rst page. Copyrights for components of this work owned by others than ACM must be honored. To copy otherwise,

distribute, republish, or post, requires prior specic permission and/or a fee. Request permissions from permissions@acm.org.

©2018 ACM. 0360-0300/2018/7-ART1 $15.00

DOI: 0000001.0000001

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:2 •S. Zhang et al.

In general, recommendation lists are generated based on user preferences, item features, user-item past

interactions and some other additional information such as temporal (e.g., sequence-aware recommender) and

spatial (e.g., POI recommender) data. Recommendation models are mainly categorized into collaborative ltering,

content-based recommender system and hybrid recommender system based on the types of input data [1].

Deep learning enjoys a massive hype at the moment. e past few decades have witnessed the tremendous

success of the deep learning (DL) in many application domains such as computer vision and speech recognition.

e academia and industry have been in a race to apply deep learning to a wider range of applications due to its

capability in solving many complex tasks while providing start-of-the-art results [

27

]. Recently, deep learning has

been revolutionizing the recommendation architectures dramatically and brings more opportunities to improve

the performance of recommender. Recent advances in deep learning based recommender systems have gained

signicant aention by overcoming obstacles of conventional models and achieving high recommendation quality.

Deep learning is able to eectively capture the non-linear and non-trivial user-item relationships, and enable the

codication of more complex abstractions as data representations in the higher layers. Furthermore, it catches

the intricate relationships within the data itself, from abundant accessible data sources such as contextual, textual

and visual information.

Pervasiveness and ubiquity of deep learning in recommender systems.

In industry, recommender sys-

tems are critical tools to enhance user experience and promote sales/services for many online websites and mobile

applications [

20

,

27

,

30

,

43

,

113

]. For example, 80 percent of movies watched on Netix came from recommenda-

tions [

43

], 60 percent of video clicks came from home page recommendation in YouTube [

30

]. Recently, many

companies employ deep learning for further enhancing their recommendation quality [

20

,

27

,

113

]. Covington

et al. [

27

] presented a deep neural network based recommendation algorithm for video recommendation on

YouTube. Cheng et al. [

20

] proposed an App recommender system for Google Play with a wide & deep model.

Shumpei et al. [

113

] presented a RNN based news recommender system for Yahoo News. All of these models

have stood the online testing and shown signicant improvement over traditional models. us, we can see that

deep learning has driven a remarkable revolution in industrial recommender applications.

e number of research publications on deep learning based recommendation methods has increased exponen-

tially in these years, providing strong evidence of the inevitable pervasiveness of deep learning in recommender

system research. e leading international conference on recommender system, RecSys

1

, started to organize

regular workshop on deep learning for recommender system

2

since the year 2016. is workshop aims to promote

research and encourage applications of deep learning based recommender system.

e success of deep learning for recommendation both in academia and in industry requires a comprehensive

review and summary for successive researchers and practitioners to beer understand the strength and weakness,

and application scenarios of these models.

What are the dierences between this survey and former ones?

Plenty of research has been done in

the eld of deep learning based recommendation. However, to the best of our knowledge, there are very few

systematic reviews which well shape this area and position existing works and current progresses. Although

some works have explored the recommender applications built on deep learning techniques and have aempted

to formalize this research eld, few has sought to provide an in-depth summary of current eorts or detail the

open problems present in the area. is survey seeks to provide such a comprehensive summary of current

research on deep learning based recommender systems, to identify open problems currently limiting real-world

implementations and to point out future directions along this dimension.

In the last few years, a number of surveys in traditional recommender systems have been presented. For

example, Su et al. [

138

] presented a systematic review on collaborative ltering techniques; Burke et al. [

8

]

1hps://recsys.acm.org/

2hp://dlrs-workshop.org/

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

Deep Learning based Recommender System: A Survey and New Perspectives •1:3

proposed a comprehensive survey on hybrid recommender system; Fern

´

andez-Tob

´

ıas et al. [

40

] and Khan et

al. [

74

] reviewed the cross-domain recommendation models; to name a few. However, there is a lack of extensive

review on deep learning based recommender system. To the extent of our knowledge, only two related short

surveys [

7

,

97

] are formally published. Betru et al. [

7

] introduced three deep learning based recommendation

models [

123

,

153

,

159

], although these three works are inuential in this research area, this survey lost sight of

other emerging high quality works. Liu et al. [

97

] reviewed 13 papers on deep learning for recommendation,

and proposed to classify these models based on the form of inputs (approaches using content information and

approaches without content information) and outputs (rating and ranking). However, with the constant advent

of novel research works, this classication framework is no longer suitable and a new inclusive framework is

required for beer understanding of this research eld. Given the rising popularity and potential of deep learning

applied in recommender system, a systematic survey will be of high scientic and practical values. We analyzed

these works from dierent perspectives and presented some new insights toward this area. To this end, over 100

studies were shortlisted and classied in this survey.

How do we collect the papers?

In this survey, we collected over a hundred of related papers. We used Google

Scholar as the main search engine, we also adopted the database, Web of Science, as an important tool to discover

related papers. In addition, we screened most of the related high-prole conferences such as NIPS, ICML, ICLR,

KDD, WWW, SIGIR, WSDM, RecSys, etc., just to name a few, to nd out the recent work. e major keywords we

used including: recommender system, recommendation, deep learning, neural networks, collaborative ltering,

matrix factorization, etc.

Contributions of this survey.

e goal of this survey is to thoroughly review literature on the advances of deep

learning based recommender system. It provides a panorama with which readers can quickly understand and step

into the eld of deep learning based recommendation. is survey lays the foundations to foster innovations in

the area of recommender system and tap into the richness of this research area. is survey serves the researchers,

practitioners, and educators who are interested in recommender system, with the hope that they will have a

rough guideline when it comes to choosing the deep neural networks to solve recommendation tasks at hand.

To summarize, the key contributions of this survey are three-folds: (1) We conduct a systematic review for

recommendation models based on deep learning techniques and propose a classication scheme to position and

organize the current work; (2) We provide an overview and summary for the state-of-the-arts. (3) We discuss the

challenges and open issues, and identify the new trends and future directions in this research eld to share the

vision and expand the horizons of deep learning based recommender system research.

e remaining of this article is organized as follows: Section 2 introduces the preliminaries for recommender

systems and deep neural networks, we also discuss the advantages and disadvantages of deep neural network

based recommendation models. Section 3 rstly presents our classication framework and then gives detailed

introduction to the state-of-the-art. Section 4 discusses the challenges and prominent open research issues.

Section 5 concludes the paper.

2 OVERVIEW OF RECOMMENDER SYSTEMS AND DEEP LEARNING

Before we dive into the details of this survey, we start with an introduction to the basic terminology and concepts

regarding recommender system and deep learning techniques. We also discuss the reasons and motivations of

introducing deep neural networks to recommender systems.

2.1 Recommender Systems

Recommender systems estimate users’ preference on items and recommend items that users might like to them

proactively [

1

,

121

]. Recommendation models are usually classied into three categories [

1

,

69

]: collaborative

ltering, content based and hybrid recommender system. Collaborative ltering makes recommendations by

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:4 •S. Zhang et al.

learning from user-item historical interactions, either explicit (e.g. user’s previous ratings) or implicit feedback (e.g.

browsing history). Content-based recommendation is based primarily on comparisons across items’ and users’

auxiliary information. A diverse range of auxiliary information such as texts, images and videos can be taken

into account. Hybrid model refers to recommender system that integrates two or more types of recommendation

strategies [8, 69].

Suppose we have

M

users and

N

items, and

R

denotes the interaction matrix and

ˆ

R

denotes the predicted

interaction matrix. Let

rui

denote the preference of user

u

to item

i

, and

ˆ

rui

denote the predicted score. Meanwhile,

we use a partially observed vector (rows of

R

)

r(u)={ru1, .. ., ruN }

to represent each user

u

, and partially observed

vector (columns of

R

)

r(i)={r1i, .. ., rMi }

to represent each item

i

.

O

and

O−

denote the observed and unobserved

interaction set. we use

U∈RM×k

and

V∈RN×k

to denote user and item latent factor.

k

is the dimension of latent

factors. In addition, sequence information such as timestamp can also be considered to make sequence-aware

recommendations. Other notations and denotations will be introduced in corresponding sections.

2.2 Deep Learning Techniques

Deep learning can be generally considered to be sub-eld of machine learning. e typical dening essence of

deep learning is that it learns deep representations, i.e., learning multiple levels of representations and abstractions

from data. For practical reasons, we consider any neural dierentiable architecture as ‘deep learning‘ as long

as it optimizes a dierentiable objective function using a variant of stochastic gradient descent (SGD). Neural

architectures have demonstrated tremendous success in both supervised and unsupervised learning tasks [

31

]. In

this subsection, we clarify a diverse array of architectural paradigms that are closely related to this survey.

•

Multilayer Perceptron (MLP) is a feed-forward neural network with multiple (one or more) hidden layers

between the input layer and output layer. Here, the perceptron can employ arbitrary activation function

and does not necessarily represent strictly binary classier. MLPs can be intrepreted as stacked layers

of nonlinear transformations, learning hierarchical feature representations. MLPs are also known to be

universal approximators.

•

Autoencoder (AE) is an unsupervised model aempting to reconstruct its input data in the output layer. In

general, the boleneck layer (the middle-most layer) is used as a salient feature representation of the input

data. ere are many variants of autoencoders such as denoising autoencoder, marginalized denoising

autoencoder, sparse autoencoder, contractive autoencoder and variational autoencoder (VAE) [15, 45].

•

Convolutional Neural Network (CNN) [

45

] is a special kind of feedforward neural network with con-

volution layers and pooling operations. It can capture the global and local features and signicantly

enhancing the eciency and accuracy. It performs well in processing data with grid-like topology.

•

Recurrent Neural Network (RNN) [

45

] is suitable for modelling sequential data. Unlike feedforward

neural network, there are loops and memories in RNN to remember former computations. Variants such

as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) network are oen deployed in

practice to overcome the vanishing gradient problem.

•

Restricted Boltzmann Machine (RBM) is a two layer neural network consisting of a visible layer and a

hidden layer. It can be easily stacked to a deep net. Restricted here means that there are no intra-layer

communications in visible layer or hidden layer.

•

Neural Autoregressive Distribution Estimation (NADE) [

81

,

152

] is an unsupervised neural network built

atop autoregressive model and feedforward neural networks. It is a tractable and ecient estimator for

modelling data distribution and densities.

•

Adversarial Networks (AN) [

46

] is a generative neural network which consists of a discriminator and

a generator. e two neural networks are trained simultaneously by competing with each other in a

minimax game framework.

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

Deep Learning based Recommender System: A Survey and New Perspectives •1:5

•

Aentional Models (AM) are dierentiable neural architectures that operate based on so content

addressing over an input sequence (or image). Aention mechanism is typically ubiquitous and was

incepted in Computer Vision and Natural Language Processing domains. However, it has also been an

emerging trend in deep recommender system research.

•

Deep Reinforcement Learning (DRL) [

106

]. Reinforcement learning operates on a trial-and-error paradigm.

e whole framework mainly consists of the following components: agents, environments, states, actions

and rewards. e combination between deep neural networks and reinforcement learning formulate

DRL which have achieved human-level performance across multiple domains such as games and self-

driving cars. Deep neural networks enable the agent to get knowledge from raw data and derive ecient

representations without handcraed features and domain heuristics.

Note that there are numerous advanced model emerging each year, here we only briey listed some important

ones. Readers who are interested in the details or more advanced models are referred to [45].

2.3 Why Deep Neural Networks for Recommendation?

Before diving into the details of recent advances, it is benecial to understand the reasons of applying deep

learning techniques to recommender systems. It is evident that numerous deep recommender systems have

been proposed in a short span of several years. e eld is indeed bustling with innovation. At this point, it

would be easy to question the need for so many dierent architectures and/or possibly even the utility of neural

networks for the problem domain. Along the same tangent, it would be apt to provide a clear rationale of why

each proposed architecture and to which scenario it would be most benecial for. All in all, this question is highly

relevant to the issue of task, domains and recommender scenarios. One of the most aractive properties of neural

architectures is that they are (1) end-to-end dierentiable and (2) provide suitable inductive biases catered to the

input data type. As such, if there is an inherent structure that the model can exploit, then deep neural networks

ought to be useful. For instance, CNNs and RNNs have long exploited the instrinsic structure in vision (and/or

human language). Similarly, the sequential structure of session or click-logs are highly suitable for the inductive

biases provided by recurrent/convolutional models [56, 143, 175].

Moreover, deep neural networks are also composite in the sense that multiple neural building blocks can be

composed into a single (gigantic) dierentiable function and trained end-to-end. e key advantage here is when

dealing with content-based recommendation. is is inevitable when modeling users/items on the web, where

multi-modal data is commonplace. For instance, when dealing with textual data (reviews [

202

], tweets [

44

]

etc.), image data (social posts, product images), CNNs/RNNs become indispensable neural building blocks. Here,

the traditional alternative (designing modality-specic features etc.) becomes signicantly less aractive and

consequently, the recommender system cannot take advantage of joint (end-to-end) representation learning. In

some sense, developments in the eld of recommender systems are also tightly coupled with advances research in

related modalities (such as vision or language communities). For example, to process reviews, one would have to

perform costly preprocessing (e.g., keyphrase extraction, topic modeling etc.) whilst newer deep learning-based

approaches are able to ingest all textual information end-to-end [

202

]. All in all, the capabilities of deep learning

in this aspect can be regarded as paradigm-shiing and the ability to represent images, text and interactions in a

unied joint framework [197] is not possible without these recent advances.

Pertaining to the interaction-only seing (i.e., matrix completion or collaborative ranking problem), the key

idea here is that deep neural networks are justied when there is a huge amount of complexity or when there is

a large number of training instances. In [

53

], the authors used a MLP to approximate the interaction function

and showed reasonable performance gains over traditional methods such as MF. While these neural models

perform beer, we also note that standard machine learning models such as BPR, MF and CML are known to

perform reasonably well when trained with momentum-based gradient descent on interaction-only data [

145

].

However, we can also consider these models to be also neural architectures as well, since they take advantage of

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:6 •S. Zhang et al.

recent deep learning advances such as Adam, Dropout or Batch Normalization [

53

,

195

]. It is also easy to see that,

traditional recommender algorithms (matrix factorization, factorization machines, etc.) can also be expressed

as neural/dierentiable architectures [

53

,

54

] and trained eciently with a framework such as Tensorow or

Pytorch, enabling ecient GPU-emabled training and free automatic dierentiation. Hence, in today’s research

climate (and even industrial), there is completely no reason to not used deep learning based tools for development

of any recommender system.

To recapitulate, we summarize the strengths of deep learning based recommendation models that readers

might bear in mind when try to employ them for practice use.

•Nonlinear Transformation

. Contrary to linear models, deep neural networks is capable of modelling

the non-linearity in data with nonlinear activations such as relu, sigmoid, tanh, etc. is property makes

it possible to capture the complex and intricate user item interaction paerns. Conventional methods

such as matrix factorization, factorization machine, sparse linear model are essentially linear models.

For example, matrix factorization models the user-item interaction by linearly combining user and item

latent factors [

53

]; Factorization machine is a member of multivariate linear family [

54

]; Obviously, SLIM

is a linear regression model with sparsity constraints. e linear assumption, acting as the basis of many

traditional recommenders, is oversimplied and will greatly limit their modelling expressiveness. It is

well-established that neural networks are able to approximate any continuous function with an arbitrary

precision by varying the activation choices and combinations [

58

,

59

]. is property makes it possible to

deal with complex interaction paerns and precisely reect user’s preference.

•Representation Learning

. Deep neural networks is ecacious in learning the underlying explanatory

factors and useful representations from input data. In general, a large amount of descriptive information

about items and users is available in real-world applications. Making use of this information provides

a way to advance our understanding of items and users, thus, resulting in a beer recommender. As

such, it is a natural choice to apply deep neural networks to representation learning in recommendation

models. e advantages of using deep neural networks to assist representation learning are in two-folds:

(1) it reduces the eorts in hand-cra feature design. Feature engineering is a labor intensive work, deep

neural networks enable automatically feature learning from raw data in unsupervised or supervised

approach; (2) it enables recommendation models to include heterogeneous content information such as

text, images, audio and even video. Deep learning networks have made breakthroughs in multimedia

data processing and shown potentials in representations learning from various sources.

•Sequence Modelling

. Deep neural networks have shown promising results on a number of sequen-

tial modelling tasks such as machine translation, natural language understanding, speech recognition,

chatbots, and many others. RNN and CNN play critical roles in these tasks. RNN achives this with

internal memory states while CNN achieves this with lters sliding along with time. Both of them are

widely applicable and exible in mining sequential structure in data. Modelling sequential signals is an

important topic for mining the temporal dynamics of user behaviour and item evolution. For example,

next-item/basket prediction and session based recommendation are typical applications. As such, deep

neural networks become a perfect t for this sequential paern mining task. is

•Flexibility

. Deep learning techniques possess high exibility, especially with the advent of many popular

deep learning frameworks such as Tensorow

3

, Keras

4

, Cae

5

, MXnet

6

, DeepLearning4j

7

, PyTorch

8

,

3hps://www.tensorow.org/

4hps://keras.io/

5hp://cae.berkeleyvision.org/

6hps://mxnet.apache.org/

7hps://deeplearning4j.org/

8hps://pytorch.org/

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

Deep Learning based Recommender System: A Survey and New Perspectives •1:7

eano

9

, etc. Most of these tools are developed in a modular way and have active community and

professional support. e good modularization makes development and engineering a lot more ecient.

For example, it is easy to combine dierent neural structures to formulate powerful hybrid models, or

replace one module with others. us, we could easily build hybrid and composite recommendation

models to simultaneously capture dierent characteristics and factors.

2.4 On Potential Limitations

Are there really any drawbacks and limitations with using deep learning for recommendation? In this section,

we aim to tackle several commonly cited arguments against the usage of deep learning for recommender systems

research.

•Interpretability.

Despite its success, deep learning is well-known to behave as black boxes, and providing

explainable predictions seem to be a really challenging task. A common argument against deep neural

networks is that the hidden weights and activations are generally non-interpretable, limiting explainability.

However, this concern has generally been eased with the advent of neural aention models and have

paved the world for deep neural models that enjoy improved interpretability [

126

,

146

,

178

]. While

interpreting individual neurons still pose a challenge for neural models (not only in recommender

systems), present state-of-the-art models are already capable of some extent of interpretability, enabling

explainable recommendation. We discuss this issue in more detail in the open issues section.

•Data Requirement.

A second possible limitation is that deep learning is known to be data-hungry, in

the sense that it requires sucient data in order to fully support its rich parameterization. However,

as compared with other domains (such as language or vision) in which labeled data is scarce, it is

relatively easy to garner a signicant amount of data within the context of recommender systems

research. Million/billion scale datasets are commonplace not only in industry but also released as

academic datasets.

•Extensive Hyperparameter Tuning.

A third well-established argument against deep learning is the

need for extensive hyperparameter tuning. However, we note that hyperparameter tuning is not an

exclusive problem of deep learning but machine learning in general (e.g., regularization factors and

learning rate similarly have to be tuned for traditional matrix factorization etc) Granted, deep learning

may introduce additional hyperparameters in some cases. For example, a recent work [

145

], aentive

extension of the traditional metric learning algorithm [60] only introduces a single hyperparameter.

3 DEEP LEARNING BASED RECOMMENDATION: STATE-OF-THE-ART

In this section, we we rstly introduce the categories of deep learning based recommendation models and then

highlight state-of-the-art research prototypes, aiming to identify the most notable and promising advancement

in recent years.

3.1 Categories of deep learning based recommendation models

To provide a bird-eye’s view of this eld, we classify the existing models based the types of employed deep

learning techniques. We further divide deep learning based recommendation models into the following two

categories. Figure 1 summarizes the classication scheme.

•

Recommendation with Neural Building Blocks. In this category, models are divided into eight subcategories

in conformity with the aforementioned eight deep learning models: MLP, AE, CNNs, RNNs, RBM, NADE,

AM, AN and DRL based recommender system. e deep learning technique in use determines the applica-

bility of recommendation model. For instance, MLP can easily model the non-linear interactions between

9hp://deeplearning.net/soware/theano/

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:8 •S. Zhang et al.

Fig. 1. Categories of deep neural network based recommendation models.

Table 1. A lookup table for reviewed publications.

Categories Publications

MLP [2, 13, 20, 27, 38, 47, 53, 54, 66, 92, 95, 157, 166, 185],

[12, 39, 93, 112, 134, 154, 182, 183]

Autoencoder [34, 88, 89, 114, 116, 125, 136, 137, 140, 159, 177, 187, 207],

[4, 10, 32, 94, 150, 151, 158, 170, 171, 188, 196, 208, 209]

CNNs [25, 49, 50, 75, 76, 98, 105, 127, 130, 153, 165, 172, 202, 206],

[6, 44, 51, 83, 110, 126, 143, 148, 169, 190, 191]

RNNs [5, 28, 35, 56, 57, 73, 78, 90, 117, 132, 139, 142, 174–176],

[24, 29, 33, 55, 68, 91, 108, 113, 133, 141, 149, 173, 179]

RBM [42, 71, 72, 100, 123, 167, 180]

NADE [36, 203, 204]

Neural Aention [14, 44, 70, 90, 99, 101, 127, 145, 169, 189, 194, 205],

[62, 146, 193]

Adversary Network [9, 52, 162, 164]

DRL [16, 21, 107, 168, 198–200]

Hybrid Models [17, 38, 41, 82, 84, 87, 118, 135, 160, 192, 193]

users and items; CNNs are capable of extracting local and global representations from heterogeneous

data sources such as textual and visual information; RNNs enable the recommender system to model the

temporal dynamics and sequential evolution of content information.

•

Recommendation with Deep Hybrid Models. Some deep learning based recommendation models utilize

more than one deep learning technique. e exibility of deep neural networks makes it possible to

combine several neural building blocks together to complement one another and form a more powerful

hybrid model. ere are many possible combinations of these night deep learning techniques but not all

have been exploited. Note that it is dierent from the hybrid deep networks in [

31

] which refer to the

deep architectures that make use of both generative and discriminative components.

Table 1 lists all the reviewed models, we organize them following the aforementioned classication scheme.

Additionally, we also summarize some of the publications from the task perspective in Table 2. e reviewed

publications are concerned with a variety of tasks. Some of the tasks have started to gain aention due to

use of deep neural networks such as session-based recommendation, image, video recommendations. Some of

the tasks might not be novel to the recommendation research area (a detail review on the side information for

recommender systems can be found in [

131

] ), but DL provides more possibility to nd beer solutions. For

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

Deep Learning based Recommender System: A Survey and New Perspectives •1:9

Table 2. Deep neural network based recommendation models in specific application fields.

Data

Sources/Tasks Notes Publications

Sequential

Information

w/t User ID [16, 29, 33, 35, 73, 91, 117, 133, 143, 160, 173, 175, 189, 194, 198, 205]

Session based

w/o User ID [55–57, 68, 73, 99, 101, 102, 117, 142, 148, 149]

Check-In, POI [150, 151, 165, 185]

Text

Hash Tags [44, 110, 118, 158, 182, 183, 193, 209]

News [10, 12, 113, 135, 169, 200]

Review texts [11, 87, 126, 146, 174, 197, 202]

otes [82, 141]

Images Visual features [2, 14, 25, 49, 50, 84, 98, 105, 112, 165, 172, 179, 191, 192, 197, 206]

Audio Music [95, 153, 167, 168]

Video Videos [14, 17, 27, 83]

Networks

Citation Network [9, 38, 66]

Social Network [32, 116, 166]

Cross Domain [39, 92, 166]

Others

Cold-start [154, 156, 170, 171]

Multitask [5, 73, 87, 174, 187]

Explainability [87, 126]

example, dealing with images and videos would be tough task without the help of deep learning techniques. e

sequence modelling capability of deep neural networks makes it easy to capture the sequential paerns of user

behaviors. Some of the specic tasks will be discussed in the following text.

3.2 Multilayer Perceptron based Recommendation

MLP is a concise but eective network which has been demonstrated to be able to approximate any measurable

function to any desired degree of accuracy [

59

]. As such, it is the basis of numerous advanced approaches and is

widely used in many areas.

Neural Extension of Traditional Recommendation Methods

. Many existing recommendation models are

essentially linear methods. MLP can be used to add nonlinear transformation to existing RS approaches and

interpret them into neural extensions.

Neural Collaborative Filtering. In most cases, recommendation is deemed to be a two-way interaction between

users preferences and items features. For example, matrix factorization decomposes the rating matrix into

low-dimensional user/item latent factors. It is natural to construct a dual neural network to model the two-way

interaction between users and items. Neural Network Matrix Factorization (NNMF) [

37

] and Neural Collaborative

Filtering (NCF) [

53

] are two representative works. Figure 2a shows the NCF architecture. Let

sus e r

u

and

sit e m

i

denote the side information (e.g. user proles and item features), or just one-hot identier of user

u

and item

i

.

e scoring function is dened as follows:

ˆ

rui =f(UT·sus er

u,VT·sit e m

i|U,V,θ)(1)

where function

f(·)

represents the multilayer perceptron, and

θ

is the parameters of this network. Traditional

MF can be viewed as a special case of NCF. erefore, it is convenient to fuse the neural interpretation of

matrix factorization with MLP to formulate a more general model which makes use of both linearity of MF and

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1:10 •S. Zhang et al.

(a) (b)

Fig. 2. Illustration of: (a) Neural Collaborative Filtering; (b) Deep Factorization Machine.

non-linearity of MLP to enhance recommendation quality. e whole network can be trained with weighted

square loss (for explicit feedback) or binary cross-entropy loss (for implicit feedback). e cross-entropy loss is

dened as:

L=−Õ

(u,i)∈O∪O−

rui log ˆ

rui +(1−ru i )log(1−ˆ

rui )(2)

Negative sampling approaches can be used to reduce the number of training unobserved instances. Follow-up

work [

112

,

134

] proposed using pairwise ranking loss to enhance the performance. He et al. [

92

,

166

] extended

the NCF model to cross-domain recommendations. Xue et al. [

184

] and Zhang et al. [

195

] showed that the one-hot

identier can be replaced with columns or rows of the interaction matrix to retain the user-item interaction

paerns.

Deep Factorization Machine. DeepFM [

47

] is an end-to-end model which seamlessly integrates factorization

machine and MLP. It is able to model the high-order feature interactions via deep neural network and low-

order interactions with factorization machine. Factorization machine (FM) utilizes addition and inner product

operations to capture the linear and pairwise interactions between features (refer to Equation (1) in [

119

] for

more details). MLP leverages the non-linear activations and deep structure to model the high-order interactions.

e way of combining MLP with FM is enlightened by wide & deep network. It replaces the wide component

with a neural interpretation of factorization machine. Compared to wide & deep model, DeepFM does not require

tedious feature engineering. Figure 2b illustrates the structure of DeepFM. e input of DeepFM

x

is an

m

-elds

data consisting of pairs

(u,i)

(identity and features of user and item). For simplicity, the outputs of FM and MLP

are denoted as yFM (x)and yM LP (x)respectively. e prediction score is calculated by:

ˆ

rui =σ(yF M (x)+yML P (x)) (3)

where σ(·) is the sigmoid activation function.

Lian et al. [

93

] improved DeepMF by proposing a eXtreme deep factorization machine to jointly model the

explicit and implicit feature interactions. e explicit high-order feature interactions are learned via a compressed

interaction network. A parallel work proposed by He et al. [

54

] replaces the second-order interactions with MLP

and proposed regularizing the model with dropout and batch normalization.

Feature Representation Learning with MLP

. Using MLP for feature representation is very straightforward

and highly ecient, even though it might not be as expressive as autoencoder, CNNs and RNNs.

Wide & Deep Learning. is general model (shown in Figure 3a) can solve both regression and classication

problems, but initially introduced for App recommendation in Google play [

20

]. e wide learning component

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Deep Learning based Recommender System: A Survey and New Perspectives •1:11

is a single layer perceptron which can also be regarded as a generalized linear model. e deep learning

component is multilayer perceptron. e rationale of combining these two learning techniques is that it enables

the recommender to capture both memorization and generalization. Memorization achieved by the wide learning

component represents the capability of catching the direct features from historical data. Meanwhile, the deep

learning component catches the generalization by producing more general and abstract representations. is

model can improve the accuracy as well as the diversity of recommendation.

Formally, the wide learning is dened as:

y=WT

wi de {x,ϕ(x)} +b

, where

WT

wi d e

,

b

are the model parameters.

e input

{x,ϕ(x)}

is the concatenated feature set consisting of raw input feature

x

and transformed (e.g. cross-

product transformation to capture the correlations between features) feature

ϕ(x)

. Each layer of the deep neural

component is in the form of

α(l+1)=f(W(l)

de e p a(l)+b(l))

, where

l

indicates the

lth

layer, and

f(·)

is the activation

function.

W(l)

de e p

and

b(l)

are weight and bias terms. e wide & deep learning model is aained by fusing these

two models:

P(ˆ

rui =1|x)=σ(WT

wi de {x,ϕ(x)} +WT

de e p a(lf)+bias )(4)

where

σ(·)

is the sigmoid function,

ˆ

rui

is the binary rating label,

a(lf)

is the nal activation. is joint model is

optimized with stochastic back-propagation ( follow-the-regularized-leader algorithm). Recommending list is

generated based on the predicted scores.

By extending this model, Chen et al. [

13

] devised a locally-connected wide & deep learning model for large

scale industrial-level recommendation task. It employs the ecient locally-connected network to replace the

deep learning component, which decreases the running time by one order of magnitude. An important step of

deploying wide & deep learning is selecting features for wide and deep parts. In other word, the system should

be able to determine which features are memorized or generalized. Moreover, the cross-product transformation

also is required to be manually designed. ese pre-steps will greatly inuence the utility of this model. e

above mentioned deep factorization based model can alleviate the eort in feature engineering.

Covington et al. [

27

] explored applying MLP in YouTube recommendation. is system divides the recom-

mendation task into two stages: candidate generation and candidate ranking. e candidate generation network

retrieves a subset (hundreds) from all video corpus. e ranking network generates a top-n list (dozens) based on

the nearest neighbors scores from the candidates. We notice that the industrial world cares more about feature

engineering (e.g. transformation, normalization, crossing) and scalability of recommendation models.

Alashkar et al. [

2

] proposed a MLP based model for makeup recommendation. is work uses two identical

MLPs to model labeled examples and expert rules respectively. Parameters of these two networks are updated

simultaneously by minimizing the dierences between their outputs. It demonstrates the ecacy of adopting

expert knowledge to guide the learning process of the recommendation model in a MLP framework. It is highly

precise even though the expertise acquisition needs a lot of human involvements.

Collaborative Metric Learning (CML). CML [

60

] replaces the dot product of MF with Euclidean distance because

dot product does not satisfy the triangle inequality of distance function. e user and item embeddings are

learned via maximizing the distance between users and their disliked items and minimizing that between users

and their preferred items. In CML, MLP is used to learn representations from item features such as text, images

and tags.

Recommendation with Deep Structured Semantic Model

. Deep Structured Semantic Model (DSSM) [

65

]

is a deep neural network for learning semantic representations of entities in a common continuous semantic

space and measuring their semantic similarities. It is widely used in information retrieval area and is supremely

suitable for top-n recommendation [

39

,

182

]. DSSM projects dierent entities into a common low-dimensional

space, and computes their similarities with cosine function. Basic DSSM is made up of MLP so we put it in this

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:12 •S. Zhang et al.

(a) (b)

Fig. 3. Illustration of: (a) Wide & Deep Learning; (b) Multi-View Deep Neural Network.

section. Note that, more advanced neural layers such as convolution and max-pooling layers can also be easily

integrated into DSSM.

Deep Semantic Similarity based Personalized Recommendation (DSPR) [

182

] is a tag-aware personalized rec-

ommender where each user

xu

and item

xi

are represented by tag annotations and mapped into a common tag

space. Cosine similarity

sim(u,i)

are applied to decide the relevance of items and users (or user’s preference over

the item). e loss function of DSPR is dened as follows:

L=−Õ

(u,i∗)

[loд(esim (u,i∗)) − loд(Õ

(u,i−)∈D−

esi m(u,i−))] (5)

where

(u,i−)

are negative samples which are randomly sampled from the negative user item pairs. e au-

thors. [

183

] further improved DSPR using autoencoder to learn low-dimensional representations from user/item

proles.

Multi-View Deep Neural Network (MV-DNN) [

39

] is designed for cross domain recommendation. It treats users

as the pivot view and each domain (suppose we have

Z

domains) as auxiliary view. Apparently, there are

Z

similarity scores for

Z

user-domain pairs. Figure 3b illustrates the structure of MV-DNN. e loss function of

MV-DNN is dened as:

L=arдmin

θ

Z

Õ

j=1

exp(γ·cosine(Yu,Ya,j))

ÍX0∈Rda exp(γ·cosine(Yu,fa(X0))) (6)

where

θ

is the model parameters,

γ

is the smoothing factor,

Yu

is the output of user view,

a

is the index of active

view.

Rda

is the input domain of view

a

. MV-DNN is capable of scaling up to many domains. However, it is

based on the hypothesis that users have similar tastes in one domain should have similar tastes in other domains.

Intuitively, this assumption might be unreasonable in many cases. erefore, we should have some preliminary

knowledge on the correlations across dierent domains to make the most of MV-DNN.

3.3 Autoencoder based Recommendation

ere exist two general ways of applying autoencoder to recommender system: (1) using autoencoder to learn

lower-dimensional feature representations at the boleneck layer; or (2) lling the blanks of the interaction

matrix directly in the reconstruction layer. Almost all the autoencoder variants such as denoising autoencoder,

variational autoencoder, contactive autoencoder and marginalized autoencoder can be applied to recommendation

task. Table 3 summarizes the recommendation models based on the types of autoencoder in use.

Autoencoder based Collaborative Filtering

. One of the successful application is to consider the collaborative

ltering from Autoencoder perspective.

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Deep Learning based Recommender System: A Survey and New Perspectives •1:13

(a) (b) (c)

Fig. 4. Illustration of: (a) Item based AutoRec; (b) Collaborative denoising autoencoder; (c) Deep collaborative filtering

framework.

AutoRec [

125

] takes user partial vectors

r(u)

or item partial vectors

r(i)

as input, and aims to reconstruct them

in the output layer. Apparently, it has two variants: item-based AutoRec (I-AutoRec) and user-based AutoRec

(U-AutoRec), corresponding to the two types of inputs. Here, we only introduce I-AutoRec, while U-AutoRec can

be easily derived accordingly. Figure 4a illustrates the structure of I-AutoRec. Given input

r(i)

, the reconstruction

is:

h(r(i)

;

θ)=f(W·д(V·r(i)+µ)+b)

, where

f(·)

and

д(·)

are the activation functions, parameter

θ={W,V,µ,b}

.

e objective function of I-AutoRec is formulated as follows:

arдmin

θ

N

Õ

i=1

kr(i)−h(r(i);θ) k2

O+λ·reg (7)

Here

k·k2

O

means that it only considers observed ratings. e objective function can be optimized by resilient

propagation (converges faster and produces comparable results) or L-BFGS (Limited-memory Broyden Fletcher

Goldfarb Shanno algorithm). ere are four important points about AutoRec that worth noticing before deploy-

ment: (1) I-AutoRec performs beer than U-AutoRec, which may be due to the higher variance of user partially

observed vectors. (2) Dierent combination of activation functions

f(·)

and

д(·)

will inuence the performance

considerably. (3) Increasing the hidden unit size moderately will improve the result as expanding the hidden

layer dimensionality gives AutoRec more capacity to model the characteristics of the input. (4) Adding more

layers to formulate a deep network can lead to slightly improvement.

CFN [

136

,

137

] is an extension of AutoRec, and posses the following two advantages: (1) it deploys the denoising

techniques, which makes CFN more robust; (2) it incorporates the side information such as user proles and item

descriptions to mitigate the sparsity and cold start inuence. e input of CFN is also partial observed vectors, so

it also has two variants: I-CFN and U-CFN, taking

r(i)

and

r(u)

as input respectively. Masking noise is imposed as

a strong regularizer to beer deal with missing elements (their values are zero). e authors introduced three

widely used corruption approaches to corrupt the input: Gaussian noise, masking noise and salt-and-pepper

noise. Further extension of CFN also incorporates side information. However, instead of just integrating side

information in the rst layer, CFN injects side information in every layer. us, the reconstruction becomes:

h({ ˜

r(i),si}) =f(W2· {д(W1· {r(i),si}+µ),si}+b)(8)

where

si

is side information,

{˜

r(i),si}

indicates the concatenation of

˜

r(i)

and

si

. Incorporating side information

improves the prediction accuracy, speeds up the training process and enables the model to be more robust.

Collaborative Denoising Auto-Encoder (CDAE). e three models reviewed earlier are mainly designed for rating

prediction, while CDAE [

177

] is principally used for ranking prediction. e input of CDAE is user partially

observed implicit feedback

r(u)

pr e f

. e entry value is 1 if the user likes the movie, otherwise 0. It can also be

regarded as a preference vector which reects user’s interests to items. Figure 4b illustrates the structure of

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:14 •S. Zhang et al.

Table 3. Summary of four autoencoder based recommendation models

Vanilla/Denoising AE Variational AE Contractive AE Marginalized AE

[114, 125, 136, 137, 159, 177]

[70, 116, 170, 171, 188] [19, 89, 94] [196] [88]

CDAE. e input of CDAE is corrupted by Gaussian noise. e corrupted input

˜

r(u)

pr e f

is drawn from a conditional

Gaussian distribution p(˜

r(u)

pr e f |r(u)

pr e f ). e reconstruction is dened as:

h(˜

r(u)

pr e f )=f(W2·д(W1·˜

r(u)

pr e f +Vu+b1)+b2)(9)

where

Vu∈RK

denotes the weight matrix for user node (see gure 4b). is weight matrix is unique for each user

and has signicant inuence on the model performance. Parameters of CDAE are also learned by minimizing the

reconstruction error:

arдmin

W1,W2,V,b1,b2

1

M

M

Õ

u=1

Ep(˜

r(u)

pr e f |r(u)

pr e f )[`(˜

r(u)

pr e f ,h(˜

r(u)

pr e f ))] +λ·reg (10)

where the loss function `(·) can be square loss or logistic loss.

CDAE initially updates its parameters using SGD over all feedback. However, the authors argued that it is

impractical to take all ratings into consideration in real world applications, so they proposed a negative sampling

technique to sample a small subset from the negative set (items with which the user has not interacted), which

reduces the time complexity substantially without degrading the ranking quality.

Muli-VAE and Multi-DAE [

94

] proposed a variant of varitional autoencoder for recommendation with implicit

data, showing beer performance than CDAE. e authors introduced a principled Bayesian inference approach

for parameters estimation and show favorable results than commonly used likelihood functions.

To the extent of our knowledge, Autoencoder-based Collaborative Filtering (ACF) [

114

] is the rst autoencoder

based collaborative recommendation model. Instead of using the original partial observed vectors, it decomposes

them by integer ratings. For example, if the rating score is integer in the range of [1-5], each

r(i)

will be divided

into ve partial vectors. Similar to AutoRec and CFN, the cost function of ACF aims at reducing the mean squared

error. However, there are two demerits of ACF: (1) it fails to deal with non-integer ratings; (2) the decomposition

of partial observed vectors increases the sparseness of input data and leads to worse prediction accuracy.

Feature Representation Learning with Autoencoder

. Autoencoder is a class of powerful feature representa-

tion learning approach. As such, it can also be used in recommender systems to learn feature representations

from user/item content features.

Collaborative Deep Learning (CDL). CDL [

159

] is a hierarchical Bayesian model which integrates stacked

denoising autoencoder (SDAE) into probabilistic matrix factorization. To seamlessly combine deep learning and

recommendation model, the authors proposed a general Bayesian deep learning framework [

161

] consisting

of two tightly hinged components: perception component (deep neural network) and task-specic component.

Specically, the perception component of CDL is a probabilistic interpretation of ordinal SDAE, and PMF acts as

the task-specic component. is tight combination enables CDL to balance the inuences of side information

and interaction history. e generative process of CDL is as follows:

(1)

For each layer

l

of the SDAE: (a) For each column

n

of weight matrix

Wl

, draw

Wl,∗n∼N(

0

,λ−1

wIDl)

; (b)

Draw the bias vector

bl∼N(

0

,λ−1

wIDl)

; (c) For each row

i

of

Xl

, draw

Xl,i∗∼N(σ(Xl−1,i∗Wl+bl),λ−1

sIDl)

.

(2)

For each item

i

: (a) Draw a clean input

Xc,i∗∼N(XL,i∗,λ−1

nIIi)

; (b) Draw a latent oset vector

ϵi∼

N(0,λ−1

vID)and set the latent item vector: Vi=ϵi+XT

L

2,i∗.

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Deep Learning based Recommender System: A Survey and New Perspectives •1:15

Fig. 5. Graphical model of collaborative deep learning (le) and collaborative deep ranking (right).

(3) Draw a latent user vector for each user u,Uu∼N(0,λ−1

uID).

(4) Draw a rating rui for each user-item pair (u,i),rui ∼N(UT

uVi,C−1

ui ).

where

Wl

and

bl

are the weight matrix and biases vector for layer

l

,

Xl

represents layer

l

.

λw

,

λs

,

λn

,

λv

,

λu

are

hyper-parameters,

Cui

is a condence parameter for determining the condence to observations [

63

]. Figure 5(le)

illustrates the graphical model of CDL. e authors exploited an EM-style algorithm to learn the parameters. In

each iteration, it updates

U

and

V

rst, and then updates

W

and

b

by xing

U

and

V

. e authors also introduced

a sampling-based algorithm [161] to avoid the local optimum.

Before CDL, Wang et al. [

158

] proposed a similar model, relational stacked denoising autoencoders (RSDAE),

for tag recommendation. e dierence of CDL and RSDAE is that RSDAE replaces the PMF with a relational

information matrix. Another extension of CDL is collaborative variational autoencoder (CVAE) [

89

], which

replaces the deep neural component of CDL with a variational autoencoder. CVAE learns probabilistic latent

variables for content information and can easily incorporate multimedia (video, images) data sources.

Collaborative Deep Ranking (CDR). CDR [

188

] is devised specically in a pairwise framework for top-n

recommendation. Some studies have demonstrated that pairwise model is more suitable for ranking lists

generation [

120

,

177

,

188

]. Experimental results also show that CDR outperforms CDL in terms of ranking

prediction. Figure 5(right) presents the structure of CDR. e rst and second generative process steps of CDR

are the same as CDL. e third and fourth steps are replaced by the following step:

•

For each user

u

: (a) Draw a latent user vector for

u

,

Uu∼N(

0

,λ−1

uID)

; (b) For each pair-wise preference

(i,j) ∈ Pi, where Pi={(i,j):rui −ru j >0}, draw the estimator, δui j ∼N(UT

uVi−UT

uVj,C−1

ui j ).

where

δui j =rui −ru j

represents the pairwise relationship of user’s preference on item

i

and item

j

,

C−1

ui j

is

a condence value which indicates how much user

u

prefers item

i

than item

j

. e optimization process is

performed in the same manner as CDL.

Deep Collaborative Filtering Framework. It is a general framework for unifying deep learning approaches with

collaborative ltering model [88]. is framework makes it easily to utilize deep feature learning techniques to

build hybrid collaborative models. e aforementioned work such as [

153

,

159

,

167

] can be viewed as special

cases of this general framework. Formally, the deep collaborative ltering framework is dened as follows:

arg min

U,V

`(R,U,V)+β(k Uk2

F+kVk2

F)+γL(X,U)+δL(Y,V)(11)

where

β

,

γ

and

δ

are trade-o parameters to balance the inuences of these three components,

X

and

Y

are side

information,

`(·)

is the loss of collaborative ltering model.

L(X,U)

and

L(Y,V)

act as hinges for connecting

deep learning and collaborative models and link side information with latent factors. On top of this framework,

the authors proposed the marginalized denoising autoencoder based collaborative ltering model (mDA-CF).

Compared to CDL, mDA-CF explores a more computationally ecient variants of autoencoder: marginalized

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:16 •S. Zhang et al.

denoising autoencoder [

15

]. It saves the computational costs for searching sucient corrupted version of input

by marginalizing out the corrupted input, which makes mDA-CF more scalable than CDL. In addition, mDA-CF

embeds content information of items and users while CDL only considers the eects of item features.

AutoSVD++ [

196

] makes use of contractive autoencoder [

122

] to learn item feature representations, then

integrates them into the classic recommendation model, SVD++ [

79

]. e proposed model posses the following

advantages: (1) compared to other autoencoders variants, contractive autoencoder captures the innitesimal

input variations; (2) it models the implicit feedback to further enhance the accuracy; (3) an ecient training

algorithm is designed to reduce the training time.

HRCD [

170

,

171

] is a hybrid collaborative model based on autoencoder and timeSVD++ [

80

]. It is a time-aware

model which uses SDAE to learn item representations from raw features and aims at solving the cold item

problem.

3.4 Convolutional Neural Networks based Recommendation

Convolution Neural Networks are powerful in processing unstructured multimedia data with convolution and

pool operations. Most of the CNNs based recommendation models utilize CNNs for feature extraction.

Feature Representation Learning with CNNs

. CNNs can be used for feature representation learning from

multiple sources such as image, text, audio, video, etc.

CNNs for Image Feature Extraction. Wang et al. [

165

] investigated the inuences of visual features to Point-

of-Interest (POI) recommendation, and proposed a visual content enhanced POI recommender system (VPOI).

VPOI adopts CNNs to extract image features. e recommendation model is built on PMF by exploring the

interactions between: (1) visual content and latent user factor; (2) visual content and latent location factor. Chu

et al. [

25

] exploited the eectiveness of visual information (e.g. images of food and furnishings of the restaurant)

in restaurant recommendation. e visual features extracted by CNN joint with the text representation are

input into MF, BPRMF and FM to test their performance. Results show that visual information improves the

performance to some degree but not signicant. He et al. [

50

] designed a visual Bayesian personalized ranking

(VBPR) algorithm by incorporating visual features (learned via CNNs) into matrix factorization. He et al. [

49

]

extended VBPR with exploring user’s fashion awareness and the evolution of visual factors that user considers

when selecting items. Yu et al. [

191

] proposed a coupled matrix and tensor factorization model for aesthetic-based

clothing recommendation, in which CNNs is used to learn the images features and aesthetic features. Nguyen

et al. [

110

] proposed a personalized tag recommendation model based on CNNs. It utilizes the convolutional

and max-pooling layer to get visual features from patches of images. User information is injected for generating

personalized recommendation. To optimize this network, the BPR objective is adopted to maximize the dierences

between the relevant and irrelevant tags. Lei et al. [

84

] proposed a comparative deep leaning model with CNNs

for image recommendation. is network consists of two CNNs which are used for image representation learning

and a MLP for user preferences modelling. It compares two images (one positive image user likes and one negative

image user dislikes) against a user. e training data is made up of triplets:

t

(user

Ut

, positive image

I+

t

, negative

image

I−

t

). Assuming that the distance between user and positive image

D(π(Ut),ϕ(I+

t))

should be closer than

the distance between user and negative images

D(π(Ut),ϕ(I−

t))

, where

D(·)

is the distance metric (e.g. Euclidean

distance). ConTagNet [

118

] is a context-aware tag recommender system. e image features are learned by

CNNs. e context representations are processed by a two layers fully-connected feedforward neural network.

e outputs of two neural networks are concatenated and fed into a somax funcation to predict the probability

of candidate tags.

CNNs for Text Feature Extraction. DeepCoNN [

202

] adopts two parallel CNNs to model user behaviors and item

properties from review texts. is model alleviates the sparsity problem and enhances the model interpretability

by exploiting rich semantic representations of review texts with CNNs. It utilizes a word embedding technique to

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Deep Learning based Recommender System: A Survey and New Perspectives •1:17

map the review texts into a lower-dimensional semantic space as well as keep the words sequences information.

e extracted review representations then pass through a convolutional layer with dierent kernels, a max-

pooling layer, and a full-connected layer consecutively. e output of the user network

xu

and item network

xi

are nally concatenated as the input of the prediction layer where the factorization machine is applied to capture

their interactions for rating prediction. Catherine et al. [

11

] mentioned that DeepCoNN only works well when

the review text wrien by the target user for the target item is available at test time, which is unreasonable. As

such, they extended it by introducing a latent layer to represent the target user-target-item pair. is model

does not access the reviews during validation/test and can still remain good accuracy. Shen et al. [

130

] built

an e-learning resources recommendation model. It uses CNNs to extract item features from text information

of learning resources such as introduction and content of learning material, and follows the same procedure

of [

153

] to perform recommendation. ConvMF [

75

] combines CNNs with PMF in a similar way as CDL. CDL

uses autoencoder to learn the item feature representations, while ConvMF employs CNNs to learn high level

item representations. e main advantage of ConvMF over CDL is that CNNs is able to capture more accurate

contextual information of items via word embedding and convolutional kernels. Tuan et al. [

148

] proposed using

CNNs to learn feature representations form item content information (e.g., name, descriptions, identier and

category) to enhance the accuracy of session based recommendation.

CNNs for Audio and Video Feature Extraction. Van et al. [

153

] proposed using CNNs to extract features from

music signals. e convolutional kernels and pooling layers allow operations at multiple timescales. is content-

based model can alleviate the cold start problem (music has not been consumed) of music recommendation. Lee

et al. [

83

] proposed extracting audio features with the prominent CNNs model ResNet. e recommendation is

performed in the collaborative metric learning framework similar to CML.

CNNs based Collaborative ltering

. Directly applying CNNs to vanilla collaborative ltering is also viable. For

example, He et al. [

51

] proposed using CNNs to improve NCF and presented the ConvNCF. It uses outer product

instead of dot product to model the user item interaction paerns. CNNs are applied over the result of outer

product and could capture the high-order correlations among embeddings dimensions. Tang et al. [

143

] presented

sequential recommendation (with user identier) with CNNs, where two CNNs (hierarchical and vertical) are

used to model the union-level sequential paerns and skip behaviors for sequence-aware recommendation.

Graph CNNs for Recommendation

. Graph convolutional Networks is a powerful tool for non-Eulcidean data

such as: social networks, knowledge graphs, protein-interaction networks, etc [

77

]. Interactions in recommen-

dation area can also be viewed as a such structured dataset (bipartite graph). us, it can also be applied to

recommendation tasks. For example, Berg et al. [

6

] proposed considering the recommendation problem as a link

prediction task with graph CNNs. is framework makes it easy to integrate user/item side information such as

social networks and item relationships into recommendation model. Ying et al. [

190

] proposed using graph CNNs

for recommendations in Pinterest

10

. is model generates item embeddings from both graph structure as well item

feature information with random walk and graph CNNs, and is suitable for very large-scale web recommender.

e proposed model has been deployed in Pinterest to address a variety of real-world recommendation tasks.

3.5 Recurrent Neural Networks based Recommendation

RNNs are extremely suitable for sequential data processing. As such, it becomes a natural choice for dealing with

the temporal dynamics of interactions and sequential paerns of user behaviours, as well as side information

with sequential signals, such as texts, audio, etc.

Session-based Recommendation without User Identier

. In many real world applications or websites, the

system usually does not bother users to log in so that it has no access to user’s identier and her long period

10hps://www.pinterest.com

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1:18 •S. Zhang et al.

consumption habits or long-term interests. However, the session or cookie mechanisms enables those systems to

get user’s short term preferences. is is a relatively unappreciated task in recommender systems due to the

extreme sparsity of training data. Recent advancements have demonstrated the ecacy of RNNs in solving this

issue [56, 142, 176].

GRU4Rec. Hidasi et al. [

56

] proposed a session-based recommendation model, GRU4Rec, based GRU (shown in

Figure 6a). e input is the actual state of session with 1-of-

N

encoding, where

N

is the number of items. e

coordinate will be 1 if the corresponding item is active in this session, otherwise 0. e output is the likelihood of

being the next in the session for each item. To eciently train the proposed framework, the authors proposed a

session-parallel mini-batches algorithm and a sampling method for output. e ranking loss which is also coined

TOP1 and has the following form:

Ls=1

S

S

Õ

j=1

σ(ˆ

rs j −ˆ

rsi )+σ(ˆ

r2

s j )(12)

where

S

is the sample size,

ˆ

rsi

and

ˆ

rs j

are the scores on negative item

i

and positive item

j

at session

s

,

σ

is the

logistic sigmoid function. e last term is used as a regularization. Note that, BPR loss is also viable. A recent

work [

55

] found that the original TOP1 loss and BPR loss dened in [

56

] suer from the gradient vanishing

problem, as such, two novel loss functions: TOP1-max and BPR-max are proposed.

e follow-up work [

142

] proposed several strategies to further improve this model: (1) augment the click

sequences with sequence preprocessing and dropout regularization; (2) adapt to temporal changes by pre-training

with full training data and ne-tuning the model with more recent click-sequences; (3) distillation the model with

privileged information with a teacher model; (4) using item embedding to decrease the number of parameters for

faster computation.

Wu et al. [

176

] designed a session-based recommendation model for real-world e-commerce website. It utilizes

the basic RNNs to predict what user will buy next based on the click history. To minimize the computation costs,

it only keeps a nite number of the latest states while collapsing the older states into a single history state. is

method helps to balance the trade-o between computation costs and prediction accuracy. adrana et al. [

117

]

presented a hierarchical recurrent neural network for session-based recommendation. is model can deal with

both session-aware recommendation when user identiers are present.

e aforementioned three session-based models do not consider any side information. Two extensions [

57

,

132

] demonstrate that side information has eect on enhancing session recommendation quality. Hidasi et

al. [

57

] introduced a parallel architecture for session-based recommendation which utilizes three GRUs to learn

representations from identity one-hot vectors, image feature vectors and text feature vectors. e outputs of

these three GRUs are weightedly concatenated and fed into a non-linear activation to predict the next items

in that session. Smirnova et al. [

132

] proposed a context-aware session-based recommender system based on

conditional RNNs. It injects context information into input and output layers. Experimental results of these two

models suggest that models incorporated additional information outperform those solely based on historical

interactions.

Despite the success of RNNs in session-based recommendation, Jannach et al. [

68

] indicated that simple

neighbourhood approach could achieve same accuracy results as GRU4Rec. Combining the neighbourhood with

RNNs methods can usually lead to best performance. is work suggests that some baselines in recent works are

not well-justied and correctly evaluated. A more comprehensive discussion can be found in [103].

Sequential Recommendation with User Identier

. Unlike session-based recommender where user identiers

are usually not present. e following studies deal with the sequential recommendation task with known user

identications.

Recurrent Recommender Network (RRN) [

175

] is a non-parametric recommendation model built on RNNs (shown

in Figure 6b). It is capable of modelling the seasonal evolution of items and changes of user preferences over time.

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Deep Learning based Recommender System: A Survey and New Perspectives •1:19

(a) (b) (c)

Fig. 6. Illustration of: (a) Session-based recommendation with RNN; (b) Recurrent recommender network; (c) Restricted

Boltzmann Machine based Collaborative Filtering.

RRN uses two LSTM networks as the building block to model dynamic user state

uut

and item state

vit

. In the

meantime, considering the xed properties such as user long-term interests and item static features, the model

also incorporates the stationary latent aributes of user and item:

uu

and

vi

. e predicted rating of item

j

given

by user iat time tis dened as:

ˆ

rui |t=f(uut ,vi t ,uu,vi)(13)

where

uut

and

vit

are learned from LSTM,

uu

and

vi

are learned by the standard matrix factorization. e

optimization is to minimize the square error between predicted and actual rating values.

Wu et al. [

174

] further improved the RRNs model by modelling text reviews and ratings simultaneously. Unlike

most text review enhanced recommendation models [

127

,

202

], this model aims to generate reviews with a

character-level LSTM network with user and item latent states. e review generation task can be viewed as an

auxiliary task to facilitate rating prediction. is model is able to improve the rating prediction accuracy, but

cannot generate coherent and readable review texts. NRT [

87

] which will be introduced in the following text can

generate readable review tips. Jing et al. [

73

] proposed a multi-task learning framework to simultaneously predict

the returning time of users and recommend items. e returning time prediction is motivated by a survival

analysis model designed for estimating the probability of survival of patients. e authors modied this model

by using LSTM to estimate the returning time of costumers. e item recommendation is also performed via

LSTM from user’s past session actions. Unlike aforementioned session-based recommendations which focus on

recommending in the same session, this model aims to provide inter-session recommendations. Li et al. [

91

]

presented a behavior-intensive model for sequential recommendation. is model consists of two components:

neural item embedding and discriminative behaviors learning. e laer part is made up of two LSTMs for

session and preference behaviors learning respectively. Christakopoulou et al. [

24

] designed an interactive

recommender with RNNs. e proposed framework aims to address two critical tasks in interactive recommender:

ask and respond. RNNs are used to tackle both tasks: predict questions that the user might ask based on her

recent behaviors(e.g, watch event) and predict the responses. Donkers et al. [

35

] designed a novel type of Gated

Recurrent Unit to explicit represent individual user for next item recommendation.

Feature Representation Learning with RNNs

. For side information with sequential paerns, using RNNs as

the representation learning tool is an advisable choice.

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:20 •S. Zhang et al.

Dai et al. [

29

] presented a co-evolutionary latent model to capture the co-evolution nature of users’ and

items’ latent features. e interactions between users and items play an important role in driving the changes

of user preferences and item status. To model the historical interactions, the author proposed using RNNs to

automatically learn representations of the inuences from dri, evolution and co-evolution of user and item

features.

Bansal et al. [

5

] proposed using GRUs to encode the text sequences into latent factor model. is hybrid

model solves both warm-start and cold-start problems. Furthermore, the authors adopted a multi-task regularizer

to prevent overing and alleviate the sparsity of training data. e main task is rating prediction while the

auxiliary task is item meta-data (e.g. tags, genres) prediction.

Okura et al. [

113

] proposed using GRUs to learn more expressive aggregation for user browsing history (browsed

news), and recommend news articles with latent factor model. e results show a signicant improvement

compared with the traditional word-based approach. e system has been fully deployed to online production

services and serving over ten million unique users everyday.

Li et al. [

87

] presented a multitask learning framework, NRT, for predicting ratings as well as generating textual

tips for users simultaneously. e generated tips provide concise suggestions and anticipate user’s experience

and feelings on certain products. e rating prediction task is modelled by non-linear layers over item and user

latent factors

U∈Rku×M

,

V∈Rkv×M

, where

ku

and

kv

(not necessarily equal) are latent factor dimensions for

users and items. e predicted rating

rui

and two latent factor matrices are fed into a GRU for tips generation.

Here,

rui

is used as context information to decide the sentiment of the generated tips. e multi-task learning

framework enables the whole model to be trained eciently in an end-to-end paradigm.

Song et al. [

135

] designed a temporal DSSM model which integrates RNNs into DSSM for recommendation.

Based on traditional DSSM, TDSSM replace the le network with item static features, and the right network with

two sub-networks to modelling user static features (with MLP) and user temporal features (with RNNs).

3.6 Restricted Boltzmann Machine based Recommendation

Salakhutdinov et al. [

123

] proposed a restricted Boltzmann machine based recommender (shown in Figure 6c).

To the best of our knowledge, it is the rst recommendation model that built on neural networks. e visible unit

of RBM is limited to binary values, therefore, the rating score is represented in a one-hot vector to adapt to this

restriction. For example, [0,0,0,1,0] represents that the user gives a rating score 4 to this item. Let

hj,j=

1

, ..., F

denote the hidden units with xed size

F

. Each user has a unique RBM with shared parameters. Suppose a user

rated

m

movies, the number of visible units is

m

, Let

X

be a

K×m

matrix where

xy

i=

1 if user

u

rated movie

i

as

yand xy

i=0 otherwise. en:

p(vy

i=1|h)=

exp(by

i+ÍF

j=1hjWy

i j )

ÍK

l=1exp(bl

i+ÍF

j=1hjWl

i j ),p(hj=1|X)=σ(bj+

m

Õ

i=1

K

Õ

y=1

xy

iWy

i j )(14)

where

Wy

i j

represents the weight on the connection between the rating

y

of movie

i

and the hidden unit

j

,

by

i

is

the bias of rating

y

for movie

i

,

bj

is the bias of hidden unit

j

. RBM is not tractable, but the parameters can be

learned via the Contrastive Divergence (CD) algorithm [

45

]. e authors further proposed using a conditional

RBM to incorporate the implicit feedback. e essence here is that users implicitly tell their preferences by giving

ratings, regardless of how they rate items.

e above RBM-CF is user-based where a given user’s rating is clamped on the visible layer. Similarity, we can

easily design an item-based RBM-CF if we clamp a given item’s rating on the visible layer. Georgiev et al. [

42

]

proposed to combine the user-based and item-based RBM-CF in a unied framework. In the case, the visible units

are determined both by user and item hidden units. Liu et al. [

100

] designed a hybrid RBM-CF which incorporates

item features (item categories). is model is also based on conditional RBM. ere are two dierences between

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

Deep Learning based Recommender System: A Survey and New Perspectives •1:21

Table 4. Categories of neural aention based recommendation models.

Vanilla Aention Co-Aention

[14, 44, 70, 90, 99, 101, 127, 145, 169, 189] [62, 146, 193, 194, 205]

this hybrid model with the conditional RBM-CF with implicit feedback: (1) the conditional layer here is modelled

with the binary item genres; (2) the conditional layer aects both the hidden layer and the visible layer with

dierent connected weights.

3.7 Neural Aention based Recommendation

Aention mechanism is motivated by human visual aention. For example, people only need to focus on specic

parts of the visual inputs to understand or recognize them. Aention mechanism is capable of ltering out the

uninformative features from raw inputs and reduce the side eects of noisy data. It is an intuitive but eective

technique and has garnered considerable aention over the recent years across areas such as computer vision [

3

],

natural language processing [

104

,

155

] and speech recognition [

22

,

23

]. Neural aention can not only used in

conjunction with MLP, CNNs and RNNs, but also address some tasks independently [

155

]. Integrating aention

mechanism into RNNs enables the RNNs to process long and noisy inputs [

23

]. Although LSTM can solve the

long memory problem theoretically, it is still problematic when dealing with long-range dependencies. Aention

mechanism provides a beer solution and helps the network to beer memorize inputs. Aention-based CNNs

are capable of capturing the most informative elements of the inputs [

127

]. By applying aention mechanism to

recommender system, one could leverage aention mechanism to lter out uninformative content and select the

most representative items [

14

] while providing good interpretability. Although neural aention mechanism is

not exactly a standalone deep neural technique, it is still worthwhile to discuss it separately due to its widespread

use.

Aention model learns to aend to the input with aention scores. Calculating the aention scores lives

at the heart of neural aention models. Based on the way for calculating the aention scores, we classify the

neural aention models into (1) standard vanilla aention and (2) co-aention. Vanilla aention utilizes a

parameterized context vector to learn to aend while co-aention is concerned with learning aention weights

from two-sequences. Self-aention is a special case of co-aention. Recent works [

14

,

44

,

127

] demonstrate the

capability of aention mechanism in enhancing recommendation performance. Table 4 summarizes the aention

based recommendation models.

Recommendation with Vanilla Attention

Chen et al. [

14

] proposed an aentive collaborative ltering model by introducing a two-level aention

mechanism to latent factor model. It consists of item-level and component-level aention. e item-level

aention is used to select the most representative items to characterize users. e component-level aention aims

to capture the most informative features from multimedia auxiliary information for each user. Tay et al. [

145

]

proposed a memory-based aention for collaborative metric learning. It introduces a latent relation vector

learned via aention to CML. Jhamb et al. [

70

] proposed using aention mechanism to improve the performance

of autoencoder based CF. Liu et al. [

99

] proposed a short-term aention and memory priority based model, in

which both long and short term user interests are intergrated for session based recommendation. Ying et al. [

189

]

proposed a hierarchical aention model for sequential recommendation. Two aention networks are used to

model user long-term and short-term interests.

Introducing aention mechanism to RNNs could signicantly improve their performance. Li et al. [

90

] proposed

such an aention-based LSTM model for hashtag recommendation. is work takes the advantages of both RNNs

and aention mechanism to capture the sequential property and recognize the informative words from microblog

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:22 •S. Zhang et al.

posts. Loyala et al. [

101

] proposed an encoder-decoder architecture with aention for user session and intents

modelling. is model consists of two RNNs and could capture the transition regularities in a more expressive

way.

Vanilla aention can also work in conjunction with CNNs for recommender tasks. Gong et al. [

44

] proposed an

aention based CNNs system for hashtag recommendation in microblog. It treats hashtag recommendation as a

multi-label classication problem. e proposed model consists of a global channel and a local aention channel.

e global channel is made up of convolution lters and max-pooling layers. All words are encoded in the input

of global channel. e local aention channel has an aention layer with given window size and threshold to

select informative words (known as trigger words in this work). Hence, only trigger words are at play in the

subsequent layers. In the follow-up work [

127

], Seo et al. made use of two neural networks same as [

44

] (without

the last two layers) to learn feature representations from user and item review texts, and predict rating scores

with dot product in the nal layer. Wang et al. [

169

] presented a combined model for article recommendation, in

which CNNs is used to learn article representations and aention is utilized to deal with the diverse variance of

editors’s selection behavior.

Recommendation with Co-Attention

Zhang et al. [

194

] proposed a combined model, ARec, which improves

the sequential recommendation performance by capitalizing the strength of both self-aention and metric learning.

It uses self-aention to learn user short-term intents from her recent interactions and takes the advantages

of metric learning to learn more expressive user and item embemddings. Zhou et al. [

205

] proposed using

self-aention for user heterogeneous behaviour modelling. Self-aention is simple yet eective mechanism and

has shown superior performance than CNNs and RNNs in terms of sequential recommendation task. We believe

that it has the capability to replace many complex neural models and more investigation is expected. Tay et

al. [

146

] proposed a review based recommendation system with multi-pointer co-aention. e co-aention

enables the model to select information reviews via co-learning from both user and item reviews. Zhang et

al. [

193

] proposed a co-atention based hashtag recommendation model that integrates both visual and textual

information. Shi et al. [62] proposed a neural co-aention model for personalized ranking task with meta-path.

3.8 Neural AutoRegressive based Recommendation

As mentioned above, RBM is not tractable, thus we usually use the Contrastive Divergence algorithm to approxi-

mate the log-likelihood gradient on the parameters [

81

], which also limits the usage of RBM-CF. e so-called

Neural Autoregressive Distribution Estimator (NADE) is a tractable distribution estimator which provides a

desirable alternative to RBM. Inspired by RBM-CF, Zheng et al. [

204

] proposed a NADE based collaborative

ltering model (CF-NADE). CF-NADE models the distribution of user ratings. Here, we present a detailed

example to illustrate how the CF-NADE works. Suppose we have 4 movies: m1 (rating is 4), m2 (rating is 2), m3

(rating is 3) and m4 (rating is 5). e CF-NADE models the joint probability of the rating vector

r

by the chain

rule:

p(r)=ÎD

i=1p(rmoi|rmo<i)

,where

D

is the number of items that the user has rated,

o

is the

D

-tuple in the

permutations of

(

1

,

2

, .. ., D)

,

mi

is the index of the

ith

rated item,

rmoi

is the rating that the user gives to item

moi

.

More specically, the procedure goes as follows: (1) the probability that the user gives

m

1 4-star conditioned on

nothing; (2) the probability that the user gives

m

2 2-star conditioned on giving

m

1 4-star; (3) the probability that

the user gives

m

3 3-star conditioned on giving

m

1 4-star and

m

2 2-star; (4) the probability that the user gives

m

4

5-star conditioned on giving m1 4-star, m2 2-star and m3 3-star.

Ideally, the order of movies should follow the time-stamps of ratings. However, empirical study shows that

random drawing also yields good performances. is model can be further extended to a deep model. In the

follow-up paper, Zheng et al. [203] proposed incorporating implicit feedback to overcome the sparsity problem

of rating matrix. Du et al. [

36

] further imporved this model with a user-item co-autoregressive approach, which

ahieves beer performance in both rating estimation and personalized ranking tasks.

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Deep Learning based Recommender System: A Survey and New Perspectives •1:23

3.9 Deep Reinforcement Learning for Recommendation

Most recommendation models consider the recommendation process as a static process, which makes it dicult

to capture user’s temporal intentions and to respond in a timely manner. In recent years, DRL has begun to garner

aention [

21

,

107

,

168

,

198

–

200

] in making personalized recommendation. Zhao et al. [

199

] proposed a DRL

framework, DEERS, for recommendation with both negative and positive feedback in a sequential interaction

seing. Zhao et al. [

198

] explored the page-wise recommendation scenario with DRL, the proposed framework

DeepPage is able to adaptively optimize a page of items based on user’s real-time actions. Zheng et al. [

200

]

proposed a news recommendation system, DRN, with DRL to tackle the following three challenges: (1) dynamic

changes of news content and user preference; (2) incorporating return paerns (to the service) of users; (3)

increase diversity of recommendations. Chen et al. [

16

] proposed a robust deep Q-learning algorithm to address

the unstable reward estimation issue with two strategies: stratied sampling replay and approximate regreed

reward. Choi et al. [

21

] proposed solving the cold-start problem with RL and bi-clustering. Munemasa et al [

107

]

proposed using DRL for stores recommendation.

Reinforcement Learning techniques such as contextual-bandit approach [

86

] had shown superior recommen-

dation performance in real-world applications. Deep neural networks increase the practicality of RL and make it

possible to model various of extra information for designing real-time recommendation strategies.

3.10 Adversarial Network based Recommendation

IRGAN [

162

] is the rst model which applies GAN to information retrieval area. Specically, the authors

demonstrated its capability in three information retrieval tasks, including: web search, item recommendation and

question answering. In this survey, we mainly focus on how to use IRGAN to recommend items.

Firstly, we introduce the general framework of IRGAN. Traditional GAN consists of a discriminator and a

generator. Likely, there are two schools of thinking in information retrieval, that is, generative retrieval and

discriminative retrieval. Generative retrieval assumes that there is an underlying generative process between

documents and queries, and retrieval tasks can be achieved by generating relevant document

d

given a query

q

.

Discriminative retrieval learns to predict the relevance score

r

given labelled relevant query-document pairs. e

aim of IRGAN is to combine these two thoughts into a unied model, and make them to play a minimax game

like generator and discriminator in GAN. e generative retrieval aims to generate relevant documents similar to

ground truth to fool the discriminative retrieval model.

Formally, let

pt ru e (d|qn,r)

refer to the user’s relevance (preference) distribution. e generative retrieval

model

pθ(d|qn,r)

tries to approximate the true relevance distribution. Discriminative retrieval

fϕ(q,d)

tries to

distinguish between relevant documents and non-relevant documents. Similar to the objective function of GAN,

the overall objective is formulated as follows:

JG∗

,D∗

=min

θmax

ϕ

N

Õ

n=1

(Ed∼ptr u e (d|qn,r)[loдD(d|qn)] +Ed∼pθ(d|qn,r)[loд(1−D(d|qn))]) (15)

where

D(d|qn)=σ(fϕ(q,d))

,

σ

represents the sigmoid function,

θ

and

ϕ

are the parameters for generative and

discriminative retrieval respectively. Parameter θand ϕcan be learned alternately with gradient descent.

e above objective equation is constructed for pointwise relevance estimation. In some specic tasks, it

should be in pairwise paradigm to generate higher quality ranking lists. Here, suppose

pθ(d|qn,r)

is given by a

somax function:

pθ(di|q,r)=exp(дθ(q,di))

Ídjexp(дθ(q,dj)) (16)

дθ(q,d)

is the chance of document

d

being generated from query

q

. In real-word retrieval system, both

дθ(q,d)

and

fϕ(q,d)

are task-specic. ey can either have the same or dierent formulations. e authors modelled

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:24 •S. Zhang et al.

them with the same function for convenience, and dene them as:

дθ(q,d)=sθ(q,d)

and

fϕ(q,d)=sϕ(q,d)

.

In the item recommendation scenario, the authors adopted the matrix factorization to formulate

s(·)

. It can be

substituted with other advanced models such as factorization machine or neural network.

He et al. [

52

] proposed an adversarial personalized ranking approach which enhances the Bayesian personalized

ranking with adversarial training. It plays a minimax game between the original BPR objective and the adversary

which add noises or permutations to maximize the BPR loss. Cai et al. [

9

] proposed a GAN based representation

learning approach for heterogeneous bibliographic network, which can eectively address the personalized

citation recommendation task. Wang et al. [

164

] proposed using GAN to generate negative samples for the

memory network based streaming recommender. Experiments show that the proposed GAN based sampler could

signicantly improve the performance.

3.11 Deep Hybrid Models for Recommendation

With the good exibility of deep neural networks, many neural building blocks can be intergrated to formalize

more powerful and expressive models. Despite the abundant possible ways of combination, we suggest that the

hybrid model should be reasonably and carefully designed for the specic tasks. Here, we summarize the existing

models that has been proven to be eective in some application elds.

CNNs and Autoencoder

. Collaborative Knowledge Based Embedding (CKE) [

192

] combines CNNs with autoen-

coder for images feature extraction. CKE can be viewed as a further step of CDL. CDL only considers item text

information (e.g. abstracts of articles and plots of movies), while CKE leverages structural content, textual content

and visual content with dierent embedding techniques. Structural information includes the aributes of items

and the relationships among items and users. CKE adopts the TransR [

96

], a heterogeneous network embedding

method, for interpreting structural information. Similarly, CKE employs SDAE to learn feature representations

from textual information. As for visual information, CKE adopts a stacked convolutional auto-encoders (SCAE).

SCAE makes ecient use of convolution by replacing the fully-connected layers of SDAE with convolutional

layers. e recommendation process is done in a probabilistic form similar to CDL.

CNNs and RNNs

. Lee et al. [

82

] proposed a deep hybrid model with RNNs and CNNs for quotes recommendation.

ote recommendation is viewed as a task of generating a ranked list of quotes given the query texts or dialogues

(each dialogue contains a sequence of tweets). It applies CNN sto learn signicant local semantics from tweets

and maps them to a distributional vectors. ese distributional vectors are further processed by LSTM to compute

the relevance of target quotes to the given tweet dialogues. e overall architecture is shown in Figure 12(a).

Zhang et al. [

193

] proposed a CNNs and RNNs based hybrid model for hashtag recommendation. Given a

tweet with corresponding images, the authors utilized CNNs to extract features from images and LSTM to learn

text features from tweets. Meanwhile, the authors proposed a co-aention mechanism to model the correlation

inuences and balance the contribution of texts and images.

Ebsesu et al. [

38

] presented a neural citation network which integrates CNNs with RNNs in a encoder-decoder

framework for citation recommendation. In this model, CNNs act as the encoder that captures the long-term

dependencies from citation context. e RNNs work as a decoder which learns the probability of a word in the

cited paper’s title given all previous words together with representations aained by CNNs.

Chen et al. [

17

] proposed an intergrated framework with CNNs and RNNs for personalized key frame (in

videos) recommendation, in which CNNs are used to learn feature representations from key frame images and

RNNs are used to process the textual features.

RNNs and Autoencoder

. e former mentioned collaborative deep learning model is lack of robustness and

incapable of modelling the sequences of text information. Wang et al. [160] further exploited integrating RNNs

and denoising autoencoder to overcome this limitations. e authors rst designed a generalization of RNNs

named robust recurrent network. Based on the robust recurrent network, the authors proposed the hierarchical

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

Deep Learning based Recommender System: A Survey and New Perspectives •1:25

Bayesian recommendation model called CRAE. CRAE also consists of encoding and decoding parts, but it replaces

feedforward neural layers with RNNs, which enables CRAE to capture the sequential information of item content

information. Furthermore, the authors designed a wildcard denoising and a beta-pooling technique to prevent

the model from overing.

RNNs with DRL

. Wang et al. [

163

] proposed combining supervised deep reinforcement learning wth RNNs

for treatment recommendation. e framework can learn the prescription policy from the indicator signal and

evaluation signal. Experiments demonstrate that this system could infer and discover the optimal treatments

automatically. We believe that this a valuable topic and benets the social good.

4 FUTURE RESEARCH DIRECTIONS AND OPEN ISSUES

Whilst existing works have established a solid foundation for deep recommender systems research, this section

outlines several promising prospective research directions. We also elaborate on several open issues, which we

believe is critical to the present state of the eld.

4.1 Joint Representation Learning from User and Item Content Information

Making accurate recommendations requires deep understanding of item characteristics and user’s actual demands

and preferences [

1

,

85

]. Naturally, this can be achieved by exploiting the abundant auxiliary information. For

example, context information tailors services and products according to user’s circumstances and surround-

ings [

151

], and mitigate cold start inuence; Implicit feedback indicates users’ implicit intention and is easier to

collect while gathering explicit feedback is a resource-demanding task. Although existing works have investigated

the ecacy of deep learning model in mining user and item proles [

92

,

196

], implicit feedback [

50

,

188

,

196

,

203

],

contextual information [

38

,

75

,

118

,

149

,

151

], and review texts [

87

,

127

,

174

,

202

] for recommendation, they do

not utilize these various side information in a comprehensive manner and take the full advantages of the available

data. Moreover, there are few works investigating users’ footprints (e.g. Tweets or Facebook posts) from social

media [

61

] and physical world (e.g. Internet of things) [

186

]. One can infer user’s temporal interests or intentions

from these side data resources while deep learning method is a desirable and powerful tool for integrating these

additional information. e capability of deep learning in processing heterogeneous data sources also brings

more opportunities in recommending diverse items with unstructured data such as textual, visual, audio and

video features.

Additionally, feature engineering has not been fully studied in the recommendation research community, but

it is essential and widely employed in industrial applications [

20

,

27

]. However, most of the existing models

require manually craed and selected features, which is time-consuming and tedious. Deep neural network is

a promising tool for automatic feature craing by reducing manual intervention [

129

]. ere is also an added

advantage of representation learning from free texts, images or data that exists in the ‘wild’ without having to

design intricate feature engineering pipelines. More intensive studies on deep feature engineering specic for

recommender systems are expected to save human eorts as well as improve recommendation quality.

An interesting forward looking research problem is how to design neural architectures that best exploits the

availability of other modes of data. One recent work potentially paving the way towards models of this nature is

the Joint Representation Learning framework [

197

]. Learning joint (possibly multi-modal representations) of

user and items will likely become a next emerging trend in recommender systems research. To this end, a deep

learning taking on this aspect would be how to design beer inductive biases (hybrid neural architectures) in an

end-to-end fashion. For example, reasoning over dierent modalities (text, images, interaction) data for beer

recommendation performance.

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:26 •S. Zhang et al.

4.2 Explainable Recommendation with Deep Learning

A common interpretation is that deep neural networks are highly non-interpretable. As such, making explainable

recommendations seem to be an uphill task. Along the same vein, it would be also natural to assume that big,

complex neural models are just ing the data with any true understanding (see subsequent section on machine

reasoning for recommendation). is is precisely why this direction is both exciting and also crucial. ere

are mainly two ways that explainable deep learning is important. e rst, is to make explainable predictions

to users, allowing them to understand the factors behind the network’s recommendations (i.e., why was this

item/service recommended?) [

126

,

178

]. e second track is mainly focused on explain-ability to the practitioner,

probing weights and activations to understand more about the model [145].

As of today, aentional models [

126

,

146

,

178

] have more or less eased the non-interpretable concerns of neural

models. If anything, aention models have instead led to greater extents of interpretability since the aention

weights not only give insights about the inner workings of the model but are also able to provide explainable

results to users. While this has been an existing direction of research ‘pre deep learning’, aentional models are

not only capable of enhancing performance but enjoys greater explainability. is further motivates the usage of

deep learning for recommendation.

Notably, it is both intuitive and natural that a model’s explainabiity and interpretability strongly relies on the

application domain and usage of content information. For example [

126

,

146

] mainly use reviews as a medium

of interpretability (which reviews led to making which predictions). Many other mediums/modalities can be

considered, such as image [18].

To this end, a promising direction and next step would to be to design beer aentional mechanisms, possibly

to the level of providing conversational or generative explanations (along the likes of [

87

]). Given that models

are already capable of highlighting what contributes to the decision, we believe that this is the next frontier.

4.3 Going Deeper for Recommendation

From former studies [

53

,

53

,

177

,

195

], we found that the performance of most neural CF models plateaus at three

to four layers. Going deeper has shown promising performance over shallow networks in many tasks [

48

,

64

],

nonetheless, going deeper in the context of deep neural network based RS remains largely unclear. If going

deeper give favorable results, how do we train the deep architecture? If not, what is the reason behind this? A

possibility is to look into auxiliary losses at dierent layers in similar spirit to [

147

] albeit hierarchically instead

of sequentially. Another possibility is to vary layer-wise learning rates for each layer of the deep network or

apply some residual strategies.

4.4 Machine Reasoning for Recommendation

ere have been numerous recent advances in machine reasoning in deep learning, oen involving reasoning over

natural language or visual input [

67

,

124

,

181

]. We believe that tasks like machine reading, reasoning, question

answering or even visual reasoning will have big impacts on the eld of recommender systems. ese tasks

are oen glazed over, given that they seem completely arbitrary and irrelevant with respect to recommender

systems. However, it is imperative that recommendater systems oen requires reasoning over a single (or

multiple) modalities (reviews, text, images, meta-data) which would eventually require borrowing (and adapting)

techniques from these related elds. Fundamentally, recommendation and reasoning (e.g., question answering)

are highly related in the sense that they are both information retrieval problems.

e single most impactful architectural innovation with neural architectures that are capable of machine

reasoning is the key idea of aention [

155

,

181

]. Notably, this key intuition have already (and very recently)

demonstrated eectiveness on several recommender problems. Tay et al. [

146

] proposed an co-aentive archi-

tecture for reasoning over reviews, and showed that dierent recommendation domains have dierent ‘evidence

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

Deep Learning based Recommender System: A Survey and New Perspectives •1:27

aggregation’ paerns. For interaction-only recommendation, similar reasoning architectures have utilized similar

co-aentive mechanisms for reasoning over meta-paths [

62

]. To this end, a next frontier for recommender

systems is possibly to adapt to situations that require multi-step inference and reasoning. A simple example

would to reason over a user’s social prole, purchases etc., reasoning over multiple modalities to recommend a

product. All in all, we can expect that reasoning architectures to start to take the foreground in recommender

system research.

4.5 Cross Domain Recommendation with Deep Neural Networks

Nowadays, many large companies oer diversied products or services to customers. For example, Google

provides us with web searches, mobile applications and news services; We can buy books, electronics and clothes

from Amazon. Single domain recommender system only focuses on one domain while ignores the user interests

on other domains, which also exacerbates sparsity and cold start problems [

74

]. Cross domain recommender

system, which assists target domain recommendation with the knowledge learned from source domains, provides

a desirable solution for these problems. One of the most widely studied topics in cross domain recommendation

is transfer learning which aims to improve learning tasks in one domain by using knowledge transferred from

other domains [

40

,

115

]. Deep learning is well suited to transfer learning as it learn high-level abstractions that

disentangle the variation of dierent domains. Several existing works [

39

,

92

] indicate the ecacy of deep learning

in catching the generalizations and dierences across dierent domains and generating beer recommendations

on cross-domain platforms. erefore, it is a promising but largely under-explored area where mores studies are

expected.

4.6 Deep Multi-Task Learning for Recommendation

Multi-task learning has led to successes in many deep learning tasks, from computer vision to natural language

processing [

26

,

31

]. Among the reviewed studies, several works [

5

,

73

,

87

,

187

] also applied multi-task learning to

recommender system in a deep neural framework and achieved some improvements over single task learning. e

advantages of applying deep neural network based multi-task learning are three-fold: (1) learning several tasks

at a time can prevent overing by generalizing the shared hidden representations; (2) auxiliary task provides

interpretable output for explaining the recommendation; (3) multi-task provides an implicit data augmentation for

alleviating the sparsity problem. Multitask can be utilized in traditional recommender system [

111

], while deep

learning enables them to be integrated in a tighter fashion. Apart from introducing side tasks, we can also deploy

the multitask learning for cross domain recommendation with each specic task generating recommendation for

each domain.

4.7 Scalability of Deep Neural Networks for Recommendation

e increasing data volumes in the big data era poses challenges to real-world applications. Consequently,

scalability is critical to the usefulness of recommendation models in real-world systems, and the time complexity

will also be a principal consideration for choosing models. Fortunately, deep learning has demonstrated to be

very eective and promising in big data analytics [

109

] especially with the increase of GPU computation power.

However, more future works should be studied on how to recommend eciently by exploring the following

problems: (1) incremental learning for non-stationary and streaming data such as large volume of incoming users

and items; (2) computation eciency for high-dimensional tensors and multimedia data sources; (3) balancing of

the model complexity and scalability with the exponential growth of parameters. A promising area of research in

this area involves knowledge distillation which have been explored in [

144

] for learning small/compact models

for inference in recommender systems. e key idea is to train a smaller student model that absorbs knowledge

from the large teacher model. Given that inference time is crucial for real time applications at a million/billion

user scale, we believe that this is another promising direction which warrants further investigation. Another

ACM Computing Surveys, Vol. 1, No. 1, Article 1. Publication date: July 2018.

1:28 •S. Zhang et al.

promising direction involves compression techniques [

128

]. e high-dimensional input data can be compressed

to compact embedding to reduce the space and computation time during model learning.

4.8 The Field Needs Beer, More Unified and Harder Evaluation

Each time a new model is proposed, it is expected that the publication oers evaluation and comparisons against

several baselines. e selection of baselines and datasets on most papers are seemingly arbitrary and authors

generally have free reign over the choices of datasets/baselines. ere are several issues with this.

Firstly, this creates an inconsistent reporting of scores, with each author reporting their own assortment of

results. Till this day, there is seemingly on consensus on a general ranking of models (Notably, we acknowledge

that the no free lunch theorem exists). Occasionally, we nd that results can be conicting and relative positions

change very frequently. For example, the scores of NCF in [

201

] is relatively ranked very low as compared to the

original paper that proposed the model [

53

]. is makes the relative benchmark of new neural models extremely

challenging. e question is how do we solve this? Looking into neighbouring elds (computer vision or natural

language processing), this is indeed perplexing. Why is there no MNIST, ImageNet or SAD for recommender

systems? As such, we believe that a suite of standardized evaluation datasets should be proposed.

We also note that datasets such as MovieLens are commonly used by many practioners in evaluating their

models. However, test splits are oen arbitrary (randomized). e second problem is that there is no control

over the evaluation procedure. To this end, we urge the recommender systems community to follow the CV/NLP

communities and establish a hidden/blinded test set in which prediction results can be only submied via a web

interface (such as Kaggle).

Finally, a third recurring problem is that there is no control over the diculty of test samples in recommender

system result. Is spliing by time the best? How do we know if test samples are either too trivial or impossible to

infer? Without designing proper test sets, we argue that it is in fact hard to estimate and measure progress of the

eld. To this end, we believe that the eld of recommender systems have a lot to learn from computer vision or

NLP communities.

5 CONCLUSION

In this article, we provided an extensive review of the most notable works to date on deep learning based

recommender systems. We proposed a classication scheme for organizing and clustering existing publications,

and highlighted a bunch of inuential research prototypes. We also discussed the advantages/disadvantages of

using deep learning techniques for recommendation tasks. Additionally, we detail some of the most pressing

open problems and promising future extensions. Both deep learning and recommender systems are ongoing hot

research topics in the recent decades. ere are a large number of new developing techniques and emerging

models each year. We hope this survey can provide readers with a comprehensive understanding towards the

key aspects of this eld, clarify the most notable advancements and shed some light on future studies.

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