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.

Micro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. Recently, graph contrastive learning (GCL) is adopted for enhancing the performance of graph neural network based micro-video recommendation. However, these GCL methods may suffer from the following problems: (1) they fail to fully exploit the potential of contrastive learning for ignoring or misjudging highly similar samples, and (2) the complementary recommendation effects between graph structure information and multi-modal feature information are not effectively utilized. In this paper, we propose a novel Cross-View Sample-Enriched Graph Contrastive Learning Network (CSGCL) for micro-video recommendation. Specifically, we build a collaborative learning view and a semantic learning view to learn node representations. For the collaborative learning view, we leverage similar nodes at the structure level to construct an effective collaborative contrastive objective. For the semantic learning view, we derive the k-nearest neighbor graph generated from multi-modal features as the semantic graphs and build a semantic contrastive objective for learning high-quality micro-video representations. Finally, a cross-view contrastive objective is designed to consider the mutually complementary recommendation effects by maximizing the agreement between the two above views. Extensive experiments on three real-world datasets demonstrate that the proposed model outperforms the baselines.