ADAPT: Fairness & diversity for sequential group recommendations

Abstract

In group recommendation systems, achieving a balance between fairness and diversity is a challenging yet crucial task, particularly in sequential settings where preferences evolve over multiple iterations. This paper introduces ADAPT, a novel framework designed to optimize fairness and diversity in sequential group recommendations. ADAPT employs two novel aggregation methods, FaDJO and DiGSFO, to equitably meet group members’ needs while promoting diverse content. In addition to the novel aggregation methods ADAPT introduces a novel definition for the inter-round diversity based on item-lists embeddings. Experimental results on three real datasets and different group formation demonstrate ADAPT's ability to optimize user satisfaction, fairness, and diversity, outperforming baseline methods in two different metrics (f-score and NDCG) and highlighting the importance of balancing these critical factors in sequential group settings.