Configurable Fairness for New Item Recommendation Considering Entry Time of Items

Recommender systems tend to excessively expose longer-standing items, resulting in significant unfairness to new items with little interaction records, despite they may possess potential to attract considerable amount of users. The existing fairness-based solutions do not specifically consider the exposure fairness of new items, for which a systematic definition also lacks, discouraging the promotion of new items or contents. In this work, we introduce a multi-degree new-item exposure fairness definition, which considers item entry-time, and also is configurable regarding different fairness requirements. We then propose a configurable new-item fairness-aware framework named CNIF, which employs two-stage training where fairness degrees are incorporated for guidance. Extensive experiments on multiple popular datasets and backbone models demonstrate that CNIF can effectively enhance fairness of the existing models regarding the exposure resources of new items (including the brand-new items with no interaction). Specifically, CNIF demonstrates a substantial advancement with a 65.59% improvement in fairness metric and a noteworthy 9.97% improvement in recommendation accuracy compared to backbone models on the KuaiRec dataset. In comparison to various fairness-based solutions, it stands out by achieving the best trade-off between fairness and recommendation accuracy, surpassing the best baseline by 14.20%.