Erheng Zhong

• Hong Kong University of Science and Technology

Factorization Machines (FM) have been recognized as an effective learning paradigm for incorporating complex relations to improve item recommendation in recommender systems. However, one open issue of FM lies in its factorized representation (latent factors) for each feature in the observed feature space, a characteristic often resulting in a large...

We tackle the blog recommendation problem in Tumblr for mobile users in this paper. Blog recommendation is challenging since most mobile users would suffer from the cold start when there are only a limited number of blogs followed by the user. Specifically to address this problem in the mobile domain, we take into account mobile apps, which typical...

While matrix factorisation models are ubiquitous in large scale recommendation and search, real time application of such models requires inner product computations over an intractably large set of item factors. In this manuscript we present a novel framework that uses the inverted index representation to exploit structural properties of sparse vect...

Content recommendation systems are typically based on one of the following paradigms: user based customization, or recommendations based on either collaborative filtering or low rank matrix factorization methods, or with systems that impute user interest profiles based on content browsing behavior and retrieve items similar to the interest profiles...

Transfer learning, which leverages knowledge from source domains to enhance learning ability in a target domain, has been proven effective in various applications. One major limitation of transfer learning is that the source and target domains should be directly related. If there is little overlap between the two domains, performing knowledge trans...

Music emotion recognition, which aims to automatically recognize the affective content of a piece of music, has become one of the key components of music searching, exploring, and social networking applications. Although researchers have given more and more attention to music emotion recognition studies, the recognition performance has come to a bo...

Many internet companies, such as Yahoo, Facebook, Google and Twitter, rely on content recommendation systems to deliver the most relevant content items to individual users through personaliza- tion. Delivering such personalized user experiences is believed to increase the long term engagement of users. While there has been a lot of progress in desi...

Time-sync video tagging aims to automatically generate tags for each video shot. It can improve the user's experience in previewing a video's timeline structure compared to traditional schemes that tag an entire video clip. In this paper, we propose a new application which extracts time-sync video tags by automatically exploiting crowdsourced comme...

Transfer learning, which aims to help learning tasks in a target domain by leveraging knowledge from auxiliary domains, has been demonstrated to be effective in different applications such as text mining, sentiment analysis, and so on. In addition, in many real-world applications, auxiliary data are described from multiple perspectives and usually...

Heterogeneous transfer learning has been proposed as a new learning strategy to improve performance in a target domain by leveraging data from other heterogeneous source domains where feature spaces can be different across different domains. In order to connect two different spaces, one common technique is to bridge feature spaces by using some co-...

One important challenge in social network analysis is how to model users’ distance as a single measure. We propose to model this distance by simultaneously exploring users’ profile attributes and local network structures. Due to the sparsity of data, where each user may interact with just a few people and only a few users provide their profile info...

Friendship prediction is an important task in social network analysis (SNA). It can help users identify friends and improve their level of activity. Most previous approaches predict users' friendship based on their historical records, such as their existing friendship, social interactions, etc. However, in reality, most users have limited friends i...

Accurate prediction of user behaviors is important for many social media applications, including social marketing, personalization, and recommendation. A major challenge lies in that although many previous works model user behavior from only historical behavior logs, the available user behavior data or interactions between users and items in a give...

We present in this paper our winning solution to Dedicated Task 1 in Nokia Mobile Data Challenge (MDC). MDC Task 1 is to infer the semantic category of a place based on the smartphone sensing data obtained at that place. We approach this task in a standard supervised learning setting: we extract discriminative features from the sensor data and use...

Demographics prediction is an important component of user profile modeling. The accurate prediction of users’ demographics can help promote many applications, ranging from web search, personalization to behavior targeting. In this paper, we focus on how to predict users’ demographics, including “gender”, “job type”, “marital status”, “age” and “num...

The study of users' social behaviors has gained much research attention since the advent of various social media such as Facebook, Renren and Twitter. A major kind of applications is to predict a user's future activities based on his/her historical social behaviors. In this paper, we focus on a fundamental task: to predict a user's future activity...

We present the ideas and methodologies that we used to address the KDD Cup 2013 challenge on author-paper identification. We firstly formulate the problem as a personalized ranking task and then propose to solve the task through a supervised learning framework. The key point is to eliminate those incorrectly assigned papers of a given author based...

Crowdsourcing is an effective method for collecting labeled data for various data mining tasks. It is critical to ensure the veracity of the produced data because responses collected from different users may be noisy and unreliable. Previous works solve this veracity problem by estimating both the user ability and question difficulty based on the k...

Modeling the dynamics of online social networks over time not only helps us understand the evolution of network structures and user behaviors, but also improves the performance of other analysis tasks, such as link prediction and community detection. Nowadays, users engage in multiple networks and form a "composite social network" by considering co...

Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-item preference data. In many real-world applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show that by transferring knowledge from some manually se...

Music emotion recognition (MER) aims to recognize the affective content of a piece of music, which is important for applications such as automatic soundtrack generation and music recommendation. MER is commonly formulated as a supervised learning problem. In practice, except for Pop music, there is little labeled data in most genres. In addition, e...

Recommender systems, especially the newly launched ones, have to deal with the data-sparsity issue, where little existing rating information is available. Recently, transfer learning has been proposed to address this problem by leveraging the knowledge from related recommender systems where rich collaborative data are available. However, most previ...

Accurate prediction of user behaviors is important for many social media applications, including social marketing, personalization and recommendation, etc. A major challenge lies in that, the available behavior data or interactions between users and items in a given social network are usually very limited and sparse (e.g., >= 99.9% empty). Many pre...

Over the years, transfer learning has received much attention in machine learning research and practice. Researchers have found that a major bottleneck associated with machine learning and text mining is the lack of high-quality annotated examples to help train a model. In response, transfer learning offers an attractive solution for this problem....

Matrix factorization (MF) has been demonstrated to be one of the most competitive techniques for collaborative filtering. However, state-of-the-art MFs do not consider contextual information, where ratings can be generated under different environments. For example, users select items under various situations, such as happy mood vs. sad, mobile vs....

As the popularity of the social media increases, as evidenced in Twitter, Facebook and China's Renren, spamming activities also picked up in numbers and variety. On social network sites, spammers often dis-guise themselves by creating fake accounts and hijack-ing normal users' accounts for personal gains. Different from the spammers in traditional...

One solution to the lack of label problem is to exploit trans- fer learning, whereby one acquires knowledge from source-domains to im- prove the learning performance in the target-domain. The main challenge is that the source and target domains may have different distributions. An open problem is how to select the available models (including algo-...

State-of-the-art learning algorithms accept data in feature vector format as input. Examples belonging to different classes may not always be easy to separate in the original feature space. One may ask: can transformation of existing features into new space reveal significant discriminative information not obvious in the original space? Since there...

The basis assumption that "training and test data drawn from the same distribution" is often violated in reality. In this paper, we propose one common solution to cover various scenarios of learning un- der "different but related distributions" in a single framework. Explicit examples include (a) sample selection bias between training and test- ing...

When labeled examples are limited and difficult to obtain, trans- fer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the assump- tion made by existing approaches, that the marginal and condi- tional probabilities are directly related between source and target domains, has limited applicab...

When the number of labeled examples is limited, tradi- tional supervised feature selection techniques often fail due to sample selection bias or unrepresentative sample prob- lem. To solve this, semi-supervised feature selection tech- niques exploit the statistical information of both labeled and unlabeled examples in the same time. However, the re...

MDC Task 1 is to infer the category of a place using the smartphone sensing data obtained at the place. We formulate this problem as a standard supervised learning task: we extract discriminative features from the sensor data and use state-of-the-art classifiers (SVM, Logistic Regression and Decision Tree Family) to build classification models. We...