Two-sided fairness-aware recommendation method under many-to-many relation schema

Abstract

With the widespread use of recommendation systems, researchers have gradually started to focus on fairness issues including two-sided fairness along with recommendation accuracy. However, these works often consider the relationship between the provider and the recommended items as one-to-one or one-to-many, without considering the situation that each item may have multiple related individuals on the provider side. In this paper, we implement a two-sided fair perception recommendation method based on fair allocation in a context where the relationship pattern between providers and items is many-to-many relationship. Specifically, on the one hand, the attention of all consumers is considered as the total exposure available to the provider and is fairly allocated to each provider based on the fairness criterion, and on the other hand, each exposure of each item is allocated to each relevant provider based on the fairness criterion, and the sum of the exposure obtained by each provider in all relevant items is taken as its final exposure. We conducted experiments on a real-world dataset, and the results show that the approach in this paper provides better two-sided fairness compared to the benchmark approach while maintaining good recommendation quality.