Yashar Deldjoo

Yashar Deldjoo
Politecnico di Bari | Poliba · Dipartimento di Ingegneria Elettrica e dell’Informazione

Assistant Professor, Politecnico di Bari, Italy

About

108
Publications
43,486
Reads
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1,235
Citations
Introduction
Since August 2019, I am officially an assistant professor at the Department of Electrical Engineering and Information Technology in Politecnico di Bari, Italy working with Prof. Tommaso di Noia. My main areas of research span a range of topics focusing largely on recommender systems. I obtained a Ph.D. title with Distinction in computer science with a specialization in Recommender Systems from Politecnico di Milano, Italy.
Additional affiliations
August 2020 - present
Politecnico di Bari
Position
  • Professor (Assistant)
Description
  • Working on various aspects of recommender systems: + Multimedia Recommender Systems (MM-RecSys) + Adversarial Machine Learning in Recommender Systems (AML-RecSys) + Federated and Privacy-Aware Recommender Systems (Privacy-RecSys) + Fairness in Recommender Systems (Fair-RecSys)
March 2020 - July 2020
Politecnico di Bari
Position
  • PostDoc Position
Description
  • Worked on various areas of recommender systems, focused on privacy-aware machine learning and security of RS. In particular, in this period we research on the topic of federated learning and adversarial machine learning and published several articles on these topics published on the AIIA Conference 2019, ImpactRS workshop at ACM RecSys conference, and a survey on topics of adversarial machine learning for recommender systems (under review as of July 2020).
January 2018 - January 2019
Università degli Studi di Milano-Bicocca
Position
  • Research Assistant
Description
  • Research collaborator working on the topic of multimedia recommender systems.
Education
November 2014 - November 2017
Politecnico di Milano
Field of study
  • Computer Science - Recommender Systems
September 2008 - August 2010
Chalmers University of Technology
Field of study
  • Electrical Engineering - Communication Engiinering
September 2007 - August 2011
University of Gothenburg
Field of study
  • English Linguistics

Publications

Publications (108)
Article
Full-text available
Recommender systems have become a popular and effective means to manage the ever-increasing amount of multimedia content available today and to help users discover interesting new items. Today's recommender systems suggest items of various media types, including audio, text, visual (images), and videos. In fact, scientific research related to the a...
Article
Full-text available
One common characteristic of research works focused on fairness evaluation (in machine learning) is that they call for some form of parity (equality) either in treatment – meaning they ignore the information about users’ memberships in protected classes during training – or in impact – by enforcing proportional beneficial outcomes to users in diffe...
Preprint
Full-text available
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. However, success has been accompanied with a major new arising challenge: many applications of machine learning (M...
Article
Full-text available
As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a s...
Conference Paper
Full-text available
In this paper we propose a new dataset, i.e., the MMTF-14K multi-faceted dataset. It is primarily designed for the evaluation of video-based recommender systems, but it also supports the exploration of other multimedia tasks such as popularity prediction, genre classification and auto-tagging (aka tag prediction). The data consists of 13,623 Hollyw...
Conference Paper
In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate the challenges associated with...
Article
Full-text available
Point-of-interest (POI) recommendation is an essential service to location-based social networks (LBSNs), benefiting both users providing them the chance to explore new locations and businesses by discovering new potential customers. These systems learn the preferences of users and their mobility patterns to generate relevant POI recommendations. P...
Preprint
Full-text available
Recommender systems can strongly influence which information we see online, e.g, on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and societ...
Chapter
This chapter studies state-of-the-art research related to multimedia recommender systems (MMRS), focusing on methods that integrate multimedia content as side information to various recommendation models. The multimedia features are then used by an MMRS to recommend either (1) media items from which the features were derived, or (2) non-media items...
Chapter
Adversarial machine learning is the research field investigating vulnerabilities inherent to machine learning systems’ design and ways to defend against them. Recently, recommender systems have been shown vulnerable to adversarial attacks that force the models to produce misleading recommendations. For instance, adversaries can attempt to push targ...
Preprint
Full-text available
Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments. A common trend in the previou...
Article
Full-text available
Recommendation services have been extensively adopted in various user-centered applications to help users navigate a vast space of possible choices. In such scenarios, data ownership is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations, read books, bought items) with a central server. Unf...
Preprint
Full-text available
Point-of-Interest (POI) recommender systems provide per-sonalized recommendations to users and help businesses attract potential customers. Despite their success, recent studies suggest that highly data-driven recommendations could be impacted by data biases, resulting in unfair outcomes for different stakeholders, mainly consumers (users) and prov...
Preprint
Full-text available
The textile and apparel industries have grown tremendously over the last years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to p...
Chapter
Full-text available
When customers’ choices may depend on the visual appearance of products (e.g., fashion), visually-aware recommender systems (VRSs) have been shown to provide more accurate preference predictions than pure collaborative models. To refine recommendations, recent VRSs have tried to recognize the influence of each item’s visual characteristic on users’...
Chapter
Full-text available
Point-of-Interest (POI) recommender systems provide personalized recommendations to users and help businesses attract potential customers. Despite their success, recent studies suggest that highly data-driven recommendations could be impacted by data biases, resulting in unfair outcomes for different stakeholders, mainly consumers (users) and provi...
Preprint
Full-text available
When customers' choices may depend on the visual appearance of products (e.g., fashion), visually-aware recommender systems (VRSs) have shown to provide more accurate preference predictions than pure collaborative models. To refine recommendations, recent VRSs have tried to recognize the influence of each item's visual characteristic on users' pref...
Preprint
Full-text available
Recommender systems (RSs) employ user-item feedback, e.g., ratings , to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning-To-Rank algorithms and, among them, the most widely adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise optimization approach. Recently, BPR has...
Conference Paper
Full-text available
Recommender systems (RSs) have widely grown thanks to the outstanding capability of providing users with accurate and tailored recommendations. Recently, public awareness and new regulations forced RS researchers and practitioners to study solutions to user privacy endangerment. This tutorial will guide the attendees through the possible threats an...
Preprint
Full-text available
In this position paper, we discuss recent applications of simulation approaches for recommender systems tasks. In particular, we describe how they were used to analyze the problem of misinformation spreading and understand which data characteristics affect the performance of recommendation algorithms more significantly. We also present potential li...
Conference Paper
Full-text available
Collaborative filtering recommender systems (CF-RSs) employ user-item feedback, e.g., ratings, purchases, or reviews, to harmonize similarities among customers and produce personalized lists of products. Being based on the benevolence of other customers, CF-RSs are vulnerable to Shilling Attacks, i.e., fake profiles injected on the platform by adve...
Chapter
This paper reports ongoing research for the definition of a data-driven self-healing system using machine learning (ML) techniques that can perform automatic and timely detection of fault types and locations. Specifically, the proposed method makes use of spectrogram-based CNN modeling of the 3-phase voltage signals. Furthermore, to keep human oper...
Preprint
Full-text available
Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning-To-Rank algorithms and, among them, the most widely adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise optimization approach. Recently, BPR has b...
Preprint
Full-text available
The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music recommenders have traditionally embraced content-based (CB) approaches. In the past years, music recommendation models that levera...
Conference Paper
Full-text available
Recent research on conversational information seeking mostly focuses on uni-modal interactions and information items. In this perspective paper, we highlight the importance of moving towards developing and evaluating multi-modal conversational information seeking (MMCIS) systems as they enable us to leverage richer context, overcome errors, and inc...
Article
Full-text available
The impact of data characteristics on the performance of classical recommender systems has been recently investigated and produced fruitful results about the relationship they have with recommendation accuracy. This work provides a systematic study on the impact of broadly chosen data characteristics (DCs) of recommender systems. This is applied to...
Conference Paper
Full-text available
Visually-aware recommender systems (VRSs) enhance the semantics of user-item interactions with visual features extracted from item images when they are available. Traditionally, VRSs leverage the representational power of pretrained convolutional neural networks (CNNs) to perform the item recommendation task. The adoption of CNNs is mainly attribut...
Article
Full-text available
Recommender systems (RSs) have attained exceptional performance in learning users' preferences and finding the most suitable products. Recent advances in adversarial machine learning (AML) in computer vision have raised interests in recommenders' security.It has been demonstrated that widely adopted model-based recommenders, e.g., BPR-MF, are not r...
Preprint
Full-text available
Visual-based recommender systems (VRSs) enhance recommendation performance by integrating users' feedback with the visual features of items' images. Recently, human-imperceptible image perturbations, defined \textit{adversarial samples}, have been shown capable of altering the VRSs performance, for example, by pushing (promoting) or nuking (demotin...
Preprint
Full-text available
Visually-aware recommender systems (VRSs) enhance the semantics of user-item interactions with visual features extracted from item images when they are available. Traditionally, VRSs leverage the representational power of pretrained convolutional neural networks (CNNs) to perform the item recommendation task. The adoption of CNNs is mainly attribut...
Conference Paper
Full-text available
This work revisits security threats on smart electrical grids and focuses on the dimensions of dependability, serviceability, and accountability, which constitute the security requirements of an SG application. The first two dimensions deal with fault diagnosis and location, while the last element tackles building the system more transparent. We pr...
Conference Paper
Full-text available
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent concerns in the digital era. After several data breaches and privacy scandals, the users are now worried about sharing their data. In the last decade, Federated Learning has...
Chapter
Full-text available
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent concerns in the digital era. After several data breaches and privacy scandals, the users are now worried about sharing their data. In the last decade, Federated Learning has...
Conference Paper
Full-text available
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, data ownership is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited lo...
Preprint
Full-text available
Recommender systems (RSs) have attained exceptional performance in learning users' preferences and finding the most suitable products. Recent advances in adversarial machine learning (AML) in computer vision have raised interests in recommenders' security. It has been demonstrated that widely adopted model-based recommenders, e.g., BPR-MF, are not...
Article
Full-text available
In Machine Learning scenarios, privacy is a crucial concern when models have to be trained with private data coming from users of a service, such as a recommender system, a location-based mobile service, a mobile phone text messaging service providing next word prediction, or a face image classification system. The main issue is that, often, data a...
Preprint
Full-text available
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent concerns in the digital era. After several data breaches and privacy scandals, the users are now worried about sharing their data. In the last decade, Federated Learning has...
Article
Full-text available
In the year 2019, the Recommender Systems Challenge [17] deals for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset which we believe is vitally important for recommendation systems research in the area of hotel search,...
Conference Paper
Full-text available
Automatic fault type classification is an important ingredient of smart electrical grids. Similar to other machine-learning models, methods developed for fault classification suffer from the issue of lack of transparency. This work sheds light on preliminary insights of an ongoing study, in which we show how feature importance measurement and featu...
Preprint
Full-text available
Recommender systems (RSs) have attained exceptional performance in learning users' preferences and helping them in finding the most suitable products. Recent advances in adversarial machine learning (AML) in the computer vision domain have raised interests in the security of state-of-the-art model-based recommenders. Recently, worrying deterioratio...
Conference Paper
Full-text available
Adversarial Machine Learning (AML) has emerged as the field of study that, starting from the identification of vulnerabilities in computer vision tasks (e.g., image classification), investigates security issues on modern machine learning (ML) recommenders. In this tutorial, we present a comprehensive overview of the application of AML techniques in...
Preprint
Full-text available
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, data ownership is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited lo...
Preprint
Full-text available
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, privacy is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations...
Conference Paper
Full-text available
Shilling attacks against collaborative filtering (CF) models are characterized by several fake user profiles mounted on the system by an adversarial party with the goal to harvest recommendation outcomes toward a malicious desire. The vulnerability of CF engines is directly tied with their heavy reliance on underlying interaction data ---like user...
Preprint
Full-text available
In Machine Learning scenarios, privacy is a crucial concern when models have to be trained with private data coming from users of a service, such as a recommender system, a location-based mobile service, a mobile phone text messaging service providing next word prediction, or a face image classification system. The main issue is that, often, data a...
Chapter
Full-text available
Recommender systems (RS) play a focal position in modern user-centric online services. Among them, collaborative filtering (CF) approaches have shown leading accuracy performance compared to content-based filtering (CBF) methods. Their success is due to an effective exploitation of similarities/correlations encoded in user interaction patterns, whi...
Conference Paper
Full-text available
Deep Learning-based (DL) image compression has shown prominent results compared to standard image compression techniques like JPEG, JPEG2000, BPG and WebP. Nevertheless, neither DL nor standard techniques generally can cope with critical real-world scenarios, with stringent performance constraints. In order to explore the nature of this gap, we fir...
Chapter
Full-text available
Video recordings are complex media types. When we watch a movie, we can effortlessly register a lot of details conveyed to us (by the author) through different multimedia channels, in particular, the audio and visual modalities. To date, majority of movie recommender systems use collaborative filtering (CF) models or content-based filtering (CBF) r...
Conference Paper
Full-text available
Recommender systems (RS) are an integral part of many online services aiming to provide an enhanced user-oriented experience. Machine learning (ML) models are nowadays broadly adopted in modern state-of-the-art approaches to recommendation, which are typically trained to maximize a user-centred utility (e.g., user satisfaction) or a business-orient...
Preprint
Full-text available
This paper presents the submission of the team MASlab-ZNU to the MMRecSys movie recommendation task, as part of MediaEval 2019. The task involved predicting average movie ratings, standard deviation of ratings, and the number of ratings by using audio and visual features extracted from trailers and the associated metadata. In the proposed work, we...
Preprint
Full-text available
With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security. To address the above issues, Federated Learning (FL) has been recently proposed as a means to leave data and computational...
Chapter
Full-text available
With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users’ personal privacy and data security. To address the above issues, Federated Learning (FL) has been recently proposed as a means to leave data and computational...
Conference Paper
Full-text available
The MediaEval 2019 Task "Multimedia for Recommender Systems" investigates the potential of leveraging multimedia content to enhance recommender systems. In this task, participants use a wealth of information from text, images, and audio to predict the success of items. Thereby, we advance the state-of-the-art of content-based recommender systems by...
Preprint
Full-text available
Fairness in recommender systems has been considered with respect to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue in a multistakeholder setting). Regardless, the concept has been commonly interpreted as some form of equality– i.e., the degree to which the system is meeting the information needs of all its users in an eq...
Preprint
Full-text available
The workshop features presentations of accepted contributions to the RecSys Challenge 2019 organized by trivago, TU Wien, Politec-nico di Bari, and Karlsruhe Institute of Technology. In the challenge, which originates from the domain of online travel recommender systems, participants had to build a click-prediction model based on user session inter...
Preprint
Full-text available
In order to improve the accuracy of recommendations, many recommender systems nowadays use side information beyond the user rating matrix, such as item content. These systems build user profiles as estimates of users' interest on content (e.g., movie genre, director or cast) and then evaluate the performance of the recommender system as a whole e.g...
Preprint
Full-text available
In order to improve the accuracy of recommendations, many recommender systems nowadays use side information beyond the user rating matrix, such as item content. These systems build user profiles as estimates of users' interest on content (e.g., movie genre, director or cast) and then evaluate the performance of the recommender system as a whole e.g...
Conference Paper
Full-text available
Fairness in recommender systems has been considered with respect to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue in a multistakeholder setting). Regardless, the concept has been commonly interpreted as some form of equality-i.e., the degree to which the system is meeting the information needs of all its users in an equ...