The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
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 recommendations that are consistent with their preferences. In this paper, we use a metric called miscalibration for measuring how a recommendation algorithm is responsive to users' true preferences and we consider how various algorithms may result in different degrees of miscalibration for different users. In particular, we conjecture that popularity bias which is a well-known phenomenon in recommendation is one important factor leading to miscalibration in recommendation. Our experimental results using two real-world datasets show that there is a connection between how different user groups are affected by algorithmic popularity bias and their level of interest in popular items. Moreover, we show that the more a group is affected by the algorithmic popularity bias, the more their recommendations are miscalibrated.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessRecommendation SystemsSimilar Papers 제목 키워드 기반
The Impact of Popularity Bias on Fairness and Calibration in Recommendation
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…
FairnessRecommendation SystemsInvestigating Popularity Bias Amplification in Recommender Systems Employed in the Entertainment Domain
Recommender systems have become an integral part of our daily online experience by analyzing past user behavior to suggest relevant content in entertainment domains such as music, movies, and books. Today, they are among…
FairnessRecommendation SystemsAddressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
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 recommendati…
FairnessRecommendation SystemsDetecting Statistically Significant Fairness Violations in Recidivism Forecasting Algorithms
Machine learning algorithms are increasingly deployed in critical domains such as finance, healthcare, and criminal justice [1]. The increasing popularity of algorithmic decision-making has stimulated interest in algorit…
Causal InferencePotential Factors Leading to Popularity Unfairness in Recommender Systems: A User-Centered Analysis
Popularity bias is a well-known issue in recommender systems where few popular items are over-represented in the input data, while majority of other less popular items are under-represented. This disparate representation…
Movie RecommendationRecommendation Systems