Multi-modal Bipartite Graph Structure Learning with Information Bottleneck for Micro-video Recommendation

Graph-based recommender systems have become prevalent in micro-video recommendation by modeling user-item interactions as a bipartite graph. However, these methods face two inherent limitations: (1) their reliance on a fixed, pre-defined graph structure makes them susceptible to noisy interactions, and (2) the multi-modal representations they learn often contain redundant information that is not discriminative enough for the recommendation task. To overcome these issues, we propose a novel Multi-modal Bipartite Graph Structure Learning network (MBGSL), which leverages the information bottleneck principle for robust micro-video recommendation. Specifically, MBGSL first learns adaptive graph structures from multi-modal content (e.g., visual, acoustic, textual) through dedicated graph learners to mitigate noise. Then, it applies an intra-modality information bottleneck to learn minimal sufficient representations within each modality and an inter-modality information bottleneck to capture distinctive information across modalities, thereby eliminating redundancy. Furthermore, the model incorporates collaborative signals through a contrastive learning objective to guide the graph structure learning process. Extensive experiments on three real-world datasets demonstrate that MBGSL achieves state-of-the-art performance, significantly surpassing existing baselines.