320 citations · 1k across the 25 of their papers we have counts for
8 papers · 1 filter
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…