Bamshad Mobasher

• Professor (Full) at DePaul University

Contextual factors can greatly influence the users' preferences in listening to music. Although it is hard to capture these factors directly, it is possible to see their effects on the sequence of songs liked by the user in his/her current interaction with the system. In this paper, we present a context-aware music recommender system which infers c...

Tutorial at The 21st Conference on User Modeling, Adaptation and Personalization (UMAP 2013), Rome, Italy, June 10-14, 2013

Social annotation systems enable the organization of online resources with user-defined keywords. The size and complexity of these systems make them excellent platforms for the application of recommender systems, which can provide personalized views of complex information spaces. Many researchers have concentrated on the important problem of tag re...

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approac...

Local news organizations face an urgent need to boost reader engagement amid declining circulation and competition from global media. Personalized news recommender systems offer a promising solution by tailoring content to user interests. Yet, conventional approaches often emphasize general preferences and may overlook nuanced or eclectic interests...

Effective recommender systems demand dynamic user understanding, especially in complex, evolving environments. Traditional user profiling often fails to capture the nuanced, temporal contextual factors of user preferences, such as transient short-term interests and enduring long-term tastes. This paper presents an assessment of Large Language Model...

Accurately modeling user preferences is crucial for improving the performance of content-based recommender systems. Existing approaches often rely on simplistic user profiling methods, such as averaging or concatenating item embeddings, which fail to capture the nuanced nature of user preference dynamics, particularly the interactions between long-...

Recommender systems (RS) have gained widespread adoption in digital platforms across a variety of domains. However, the users' understanding of how these systems function and how they might adjust it typically remains opaque. When users perceive a lack of ability to control or personalize the system, this can lead to a loss of trust and lower perce...

Accurately modeling user preferences is vital not only for improving recommendation performance but also for enhancing transparency in recommender systems. Conventional user profiling methods, such as averaging item embeddings, often overlook the evolving, nuanced nature of user interests, particularly the interplay between short-term and long-term...

Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly problematic over time as a few items are repeatedly over-represented in recommendation lists, leading to a feedback loop that further amplifies this bias. Although extensive r...

Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations generated by the system. Standard methods for mitigating miscalibration typically assume that user preference profiles are static, and they measure calibration relative...

Exposure bias is a well-known issue in recommender systems where the exposure is not fairly distributed among items in the recommendation results. This is especially problematic when bias is amplified over time as a few items (e.g., popular ones) are repeatedly over-represented in recommendation lists and users' interactions with those items will a...

During the past two decades, Local newspapers have been experiencing major declines in readership due to the proliferation of national and global online news sources. Local media companies, whose predominant business model is subscription-based, need to increase user engagement to provide added value for local subscribers. Personalization and recom...

During the past two decades, Local newspapers have been experiencing major declines in readership due to the proliferation of national and global online news sources. Local media companies, whose predominant business model is subscription-based, need to increase user engagement to provide added value for local subscribers. Personalization and recom...

Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when bias is amplified over time as a few items (e.g., popular ones) are repeatedly over-represented in recommendation lists and users' interactions with those items will ampl...

Competitive online games use rating systems for matchmaking; progression-based algorithms that estimate the skill level of players with interpretable ratings in terms of the outcome of the games they played. However, the overall experience of players is shaped by factors beyond the sole outcome of their games. In this paper, we engineer several fea...

American local newspapers have been experiencing a large loss of reader retention and business within the past 15 years due to the proliferation of online news sources and social media. This has led to a disturbing trend where local journalism and local news outlets are being forced out of business often leaving whole communities without a key sour...

American local newspapers have been experiencing a large loss of reader retention and business within the past 15 years due to the proliferation of online news sources. Local media companies are starting to shift from an advertising-supported business model to one based on subscriptions to mitigate this problem. With this subscription model, there...

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness, where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakehold...

In this report, we offer a brief overview of the contributions and takeaways from the Joint KaRS & ComplexRec Workshop, co-located with the 15 th edition of the ACM RecSys in Amsterdam, The Netherlands. With this workshop, we aimed to merge the main objectives envisioned for the 3 rd Edition of the Workshop of Knowledge-aware and Conversational Rec...

Competitive online games use rating systems to match players with similar skills to ensure a satisfying experience for players. In this paper, we focus on the importance of addressing different aspects of playing behavior when modeling players for creating match-ups. To this end, we engineer several behavioral features from a dataset of over 75,000...

Recommender systems have become a ubiquitous part of modern web applications. They help users discover new and relevant items. Today's users, through years of interaction with these systems have developed an inherent understanding of how recommender systems function, what their objectives are, and how the user might manipulate them. We describe thi...

Recommender systems have become a ubiquitous part of modern web applications. They help users discover new and relevant items. Today's users, through years of interaction with these systems have developed an inherent understanding of how recommender systems function, what their objectives are, and how the user might manipulate them. We describe thi...

Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when bias is amplified over time as a few popular items are repeatedly over-represented in recommendation lists. This phenomenon can be viewed as a recommendation feedback loo...

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholde...

One of the main goals of online competitive games is increasing player engagement by ensuring fair matches. These games use rating systems for creating balanced match-ups. Rating systems leverage statistical estimation to rate players' skills and use skill ratings to predict rank before matching players. Skill ratings of individual players can be a...

Online competitive games have become a mainstream entertainment platform. To create a fair and exciting experience, these games use rating systems to match players with similar skills. While there has been an increasing amount of research on improving the performance of these systems, less attention has been paid to how their performance is evaluat...

This paper proposes a vision and research agenda for the next generation of news recommender systems (RS), called the table d'hote approach. A table d'hote (translates as host's table) meal is a sequence of courses that create a balanced and enjoyable dining experience for a guest. Likewise, we believe news RS should strive to create a similar expe...

Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail, less popular, items. The effectiven...

2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT'20), 14-17 December 2020, Melbourne, Australia.

It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for t...

Nowadays, the use of social information is extending to more and more application domains. In the field of recommender systems, this information has been exploited in different ways to address some problems, especially associated with collabora-tive filtering methods, and thus achieve more reliable recom-mendations. Specifically, social tagging is...

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that are looking to provide personalized interaction to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increa...

Recently there has been a growing interest in fairness-aware recommender systems including fairness in providing consistent performance across different users or groups of users. A recommender system could be considered unfair if the recommendations do not fairly represent the tastes of a certain group of users while other groups receive recommenda...

Online competitive games have become increasingly popular. To ensure an exciting and competitive environment, these games routinely attempt to match players with similar skill levels. Matching players is often accomplished through a rating system. There has been an increasing amount of research on developing such rating systems. However, less atten...

Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored. These recommendations are then consumed by the users, their reaction will be logged and added to the system: what is generally known as a feedback loop. In this paper, we propose a method...

Popularity bias is a well-known phenomenon in recommender systems: popular items are recommended even more frequently than their popularity would warrant, amplifying long-tail effects already present in many recommendation domains. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long...

Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains where it may be desirable to mitigate the popularity bias and to improve the coverage of long-tail items in recommendations given to users. This is especially important in multistakeholder recommendation scenarios where it may...

Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains where it may be desirable to mitigate the popularity bias and to improve the coverage of long-tail items in recommendations given to users. This is especially important in multistakeholder recommendation scenarios where it may...

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized a...

Recommender systems are often biased toward popular items. In other words, few items are frequently recommended while the majority of items do not get proportionate attention. That leads to low coverage of items in recommendation lists across users (i.e. low aggregate diversity) and unfair distribution of recommended items. In this paper, we introd...

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing , researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized...

The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and racial or ethnic populations. This varying degree of performance could impact users' trust in the system and may pose legal and ethical issues in domains where fairness a...

Recommender systems have become a mainstay of modern inter-net applications. They help users identify products to purchase on Amazon, movies to watch on Netflix and songs to enjoy on Pandora. Indeed, they have become so commonplace that users, through years of interactions with these systems, have developed an inherent understanding of how recommen...

Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user groups. In this paper, we examine...

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused context-aware recommender systems (CARS), man...

Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward and static scenarios: given information about a user's past item preferences in isolation, can we predict whether they will like a new item or r...

Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations. Research in this area has been mainly focusing on finding ways to tackle this issue by increasing the number of recommended long-tail items or oth...

Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or revers...

Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or revers...

Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations. Research in this area has been mainly focusing on finding ways to tackle this issue by increasing the number of recommended long-tail items or oth...

It is well known that explicit user ratings in recommender systems are biased towards high ratings, and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for...

ACM UMAP - User Modelling, Adaptation and Personalization is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. The Theory, Opinion and Reflection (TOR) track at UMAP is designed to highlight emerging a...

It is our great pleasure to welcome you to the Second FairUMAP workshop at UMAP 2019. This full-day workshop brings together researchers working at the intersection of user modeling, adaptation, and personalization on one hand, and bias, fairness and transparency in algorithmic systems on the other hand. The workshop was motivated by the observatio...

In a variety of online settings involving interaction with end-users it is critical for the systems to adapt to changes in user preferences. User preferences on items tend to change over time due to a variety of factors such as change in context, the task being performed, or other short-term or long-term external factors. Recommender systems need t...

Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system. Identifying influential users and studying their impact on other users is an important problem because it provides insight into how small groups...

In this article, we describe DePaul University's summer data science academy for Chicago Public School students which was, in part, funded through the SIGKDD Impact Award program in 2018. The goal of the academy was to increase awareness about data science among high school students. The program specifically aimed to broaden participation of underr...

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular , niche products, are recommended rarely or not at all. However, recommending the ignored products in the "long tail" is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized divers...

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the `long tail' is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized diversi...

Recommender systems help users find relevant items of interest based on the past preferences of those users. In many domains, however, the tastes and preferences of users change over time due to a variety of factors and recommender systems should capture these dynamics in user preferences in order to remain tuned to the most current interests of us...

Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward scenarios. In reality, recommendation is often a more complex problem: it is usually just a single step in the user's more complex background ne...

The FairUMAP Workshop at UMAP 2018 brought together researchers working at the intersection of user modeling, adaptation, and personalization on the one hand, and bias and fairness in machine learning on the other hand.

Suppliers of music streaming services are showing an increasing interest for providing users with reliable personalized recommendations since their practically unlimited offerings make it difficult for users to find the music they like. In this work, we take advantage of social tags that users give to music through streaming platforms for improving...

Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all. However, those ignored products are exactly the products that businesses need to find customers for and their recommendations would be more beneficial. In this pap...

Recommendation algorithms for ratings prediction and item ranking have steadily matured during the past decade. However, these state-of-the-art algorithms are typically applied in relatively straightforward scenarios. In reality, recommendation is often a more complex problem: it is usually just a single step in the user's more complex background n...

Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable recommendations. In this paper, we introduce a flexible regularization-based framework to enhance the long-t...

Much of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the effectiveness of a recommender system. However, methods that attempt to increase diversity of recommend...