Regulating Group Exposure for Item Providers in Recommendation

Abstract

Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendation services, the interests of those providers should be valued. In this paper, we consider providers as grouped based on a common characteristic in settings in which certain provider groups have low representation of items in the catalog and, thus, in the user interactions. Then, we envision a scenario wherein platform owners seek to control the degree of exposure to such groups in the recommendation process. To support this scenario, we rely on disparate exposure measures that characterize the gap between the share of recommendations given to groups and the target level of exposure pursued by the platform owners. We then propose a re-ranking procedure that ensures desired levels of exposure are met. Experiments show that, while supporting certain groups of providers by rendering them with the target exposure, beyond-accuracy objectives experience significant gains with negligible impact in recommendation utility.

arXiv commentAccepted at the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2022)

References in corpus (7)

• Facial Expression Analysis under Partial Occlusion: A Survey
• Fairness of Exposure in Rankings
• FA*IR: A Fair Top-k Ranking Algorithm
• Equity of Attention: Amortizing Individual Fairness in Rankings
• A Theoretical Analysis of NDCG Type Ranking Measures
• Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems
• Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations