320 citations · 1.3k across the 27 of their papers we have counts for
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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.…
Crank up the volume: preference bias amplification in collaborative recommendation
Kun Lin, Nasim Sonboli, Bamshad Mobasher +1
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,…
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
Zhu Sun, Qing Guo, Jie Yang +4
Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data spa…
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison
Masoud Mansoury, Bamshad Mobasher, Robin Burke +1
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 gro…
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…
Flatter is better: Percentile Transformations for Recommender Systems
Masoud Mansoury, Robin Burke, Bamshad Mobasher
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. Implemen…
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…
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…