FINDUS: A Fairness-oriented Interaction-aware Disadvantaged Subgroup Detection Approach for Recommender Systems

An unfair recommender system can exacerbate social biases, degrade user experience, and even pose legal and ethical risks. Therefore, detecting disadvantaged subgroups of users who exhibit significant underperformance is important for optimizing and improving recommender systems. Existing methods only identify Attribute-based Disadvantaged Subgroups ( ADS s), neglecting disadvantaged subgroups caused by the interaction process between users and items (Interaction correlated Disadvantaged Subgroups, IDS s). To address this limitation, we propose FINDUS , a fairness-oriented interaction aware disadvantaged subgroup detection approach for recommender systems. This is the first work capable of detecting IDS s. In FINDUS , a collaborative grouping mechanism is used to detect IDS s. It is based on multi round bidirectional mapping between users and items to generate interaction attributes. To overcome the attribute explosion problem caused by combinations of attribute constraints during subgroup detection, we also devise a scoring and pruning method founded on upper-bound estimation. Extensive experiments are carried out on multiple real world datasets, and the results demonstrate that FINDUS can effectively and efficiently identify a diverse set of unfairly treated user subgroups, including both ADS s and IDS s.