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

Multi-modal recommender systems (MRSs) have emerged as critical multi-modal technologies on online platforms, but do we truly leverage multi-modal content properly? Through an empirical study of four diverse, real-world 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 multi-modal 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-theart MRSs and effectively addresses the User-side Content Bias. We provide our code at anonymous link.