Title: Refined Mixed-Strategy Multi-modal Representation Learning for Recommendation
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Multi-modal, Recommendation, Mix-strategy, Heterogeneous

Abstract: Representations of users and items are crucial in recommendation systems as they capture the latent relation of user-item interactions. Recent multi-modal recommendation models leveraging multi-modal features have effectively improved recommendation performance. However, existing methods lack the exploration of multiple potential relations between users and multi-modal items effectively. This paper introduces an approach called Refined Mixed Strategy Multi-modal recommender (RMSM), which aims to model the interaction between users and multi-modal items by creating refined multi-modal heterogeneous graphs and learning multiple relations through mix-strategy. Specifically, we construct an interaction graph that encompasses diverse modalities between users and items. Subsequently, RMSM leverages mix-strategy across multiple graph types to derive comprehensive representations. Extensive experiments on three public datasets show that RMSM can achieve the best results compared with baselines. Numerous ablation studies and visualization are performed on RMSM to confirm its efficacy in the context of recommendation, specifically focusing on its performance within heterogeneous and homogeneous sub-graphs.