Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
This addresses the multistakeholder impact of popularity bias for users and suppliers in recommendation domains, representing an incremental advancement.
The paper tackles the problem of popularity bias in recommender systems by proposing popularity calibration to match item popularity distributions between user profiles and recommendations, and shows that their algorithm outperforms state-of-the-art approaches on music and movie datasets while also improving supplier fairness.
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-tail items overall. The effectiveness of these approaches, however, has not been assessed in multistakeholder environments where in addition to the users who receive the recommendations, the utility of the suppliers of the recommended items should also be considered. In this paper, we propose the concept of popularity calibration which measures the match between the popularity distribution of items in a user's profile and that of the recommended items. We also develop an algorithm that optimizes this metric. In addition, we demonstrate that existing evaluation metrics for popularity bias do not reflect the performance of the algorithms when it is measured from the perspective of different stakeholders. Using music and movie datasets, we empirically show that our approach outperforms the existing state-of-the-art approaches in addressing popularity bias by calibrating the recommendations to users' preferences. We also show that our proposed algorithm has a secondary effect of improving supplier fairness.