Ensuring User-side Fairness in Dynamic Recommender Systems

arXiv:2308.15651v2 Announce Type: replace-cross
Abstract: User-side group fairness is crucial for modern recommender systems, aiming to alleviate performance disparities among user groups defined by sensitive attributes like gender, race, or age. In the ever-evolving landscape of user-item interactions, continual adaptation to newly collected data is crucial for recommender systems to stay aligned with the latest user preferences. However, we observe that such continual adaptation often exacerbates performance disparities. This necessitates a thorough investigation into user-side fairness in dynamic recommender systems, …

abstract age arxiv continual cs.cy cs.ir cs.lg data dynamic fairness gender interactions landscape modern performance race recommender systems systems type