320 citations · 1k across the 25 of their papers we have counts for
27 papers
Who Are We Recommending To? Recommender Systems in the Agentic Web
Himan Abdollahpouri, Kyle Kretschman, Sai Ravindranath +2
For two decades, recommender systems have been designed under the assumption that a human directly consumes each recommendation: receiving, interpreting, and acting upon it. The em…
Calibrated Recommendations with Contextual Bandits
Diego Feijer, Himan Abdollahpouri, Sanket Gupta +7
Spotify's Home page features a variety of content types, including music, podcasts, and audiobooks. However, historical data is heavily skewed toward music, making it challenging t…
Long-term Off-Policy Evaluation and Learning
Yuta Saito, Himan Abdollahpouri, Jesse Anderton +2
Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. A known example is a click-bait algorithm, which may increase short-term clicks but da…
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation
Masoud Mansoury, Himan Abdollahpouri, Bamshad Mobasher +3
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…
A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy +2
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 fairne…
Toward the Next Generation of News Recommender Systems
Himan Abdollahpouri, Edward Malthouse, Joseph Konstan +2
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 ta…
User-centered Evaluation of Popularity Bias in Recommender Systems
Himan Abdollahpouri, Masoud Mansoury, Robin Burke +2
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 oth…
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke +1
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…
Popularity Bias in Recommendation: A Multi-stakeholder Perspective
Himan Abdollahpouri
Traditionally, especially in academic research in recommender systems, the focus has been solely on the satisfaction of the end-user. While user satisfaction has, indeed, been asso…
Feedback Loop and Bias Amplification in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy +2
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 recommendati…
Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
Himan Abdollahpouri, Masoud Mansoury, Robin Burke +1
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 effe…
Multi-sided Exposure Bias in Recommendation
Himan Abdollahpouri, Masoud Mansoury
Academic research in recommender systems has been greatly focusing on the accuracy-related measures of recommendations. Even when non-accuracy measures such as popularity bias, div…
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy +2
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…
Unfair Exposure of Artists in Music Recommendation
Himan Abdollahpouri, Robin Burke, Masoud Mansoury
Fairness in machine learning has been studied by many researchers. In particular, fairness in recommender systems has been investigated to ensure the recommendations meet certain c…
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Jessie Smith +3
The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and…
The Relationship between the Consistency of Users' Ratings and Recommendation Calibration
Masoud Mansoury, Himan Abdollahpouri, Joris Rombouts +1
Fairness in recommender systems has recently received attention from researchers. Unfair recommendations have negative impact on the effectiveness of recommender systems as it may…
The Impact of Popularity Bias on Fairness and Calibration in Recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke +1
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.…
Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness
Himan Abdollahpouri, Robin Burke
There is growing research interest in recommendation as a multi-stakeholder problem, one where the interests of multiple parties should be taken into account. This category subsume…
The Unfairness of Popularity Bias in Recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke +1
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…
Reducing Popularity Bias in Recommendation Over Time
Himan Abdollahpouri, Robin Burke
Many recommendation algorithms suffer from popularity bias: a small number of popular items being recommended too frequently, while other items get insufficient exposure. Research…
Incorporating System-Level Objectives into Recommender Systems
Himan Abdollahpouri
One of the most essential parts of any recommender system is personalization-- how acceptable the recommendations are from the user's perspective. However, in many real-world appli…
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke +5
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tas…
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
Himan Abdollahpouri, Robin Burke, Bamshad Mobasher
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, re…
Towards Effective Exploration/Exploitation in Sequential Music Recommendation
Himan Abdollahpouri, Steve Essinger
Music streaming companies collectively serve billions of songs per day. Radio-based music services may intersperse audio advertisements among the songs as a means to generate reven…