Unveiling the Impact of Multi-modal Content in Multi-modal Recommender Systems

摘要

Multi-modal recommender systems (MRSs) have emerged as critical multi-modal technologies, but Do we truly leverage multi-modal content properly? Through an empirical study of four diverse, realworld datasets spanning various recommendation scenarios, we observe that MRSs exhibit a stronger tendency to recommend items with high similarity to users' past interactions in terms of multimodal content than conventional RSs. While this tendency improves the recommendation accuracy, it introduces a previously unexplored bias that significantly impacts user experience. We define this bias as User-side Content Bias: users who prefer items similar to their historical choices receive higher quality recommendations than those seeking diverse options. We show that User-side Content Bias is unrelated to the activity of users, indicating a fundamental limitation in current MRSs. We propose ISOLATOR: utIlizing uSer-side cOntent simiLarity via a model-AgnosTic framewORk to leverage multi-modal content more properly. ISOLATOR estimates the impact of User-side Content Similarity and proposes two intervention strategies to meet the needs for more accurate and unbiased recommendations. Extensive evaluations on several widely used datasets demonstrate that ISOLATOR consistently improves various state-of-the-art MRSs and effectively addresses the User-side Content Bias.