Compensating Information and Capturing Modal Preferences in Multimodal Recommendation: A Dual-Path Representation Learning Framework

In the context of information explosion, multimodal recommender systems (MMRS) have demonstrated great potential in capturing users' complex preferences and enhancing recommendation performance by integrating multimodal data such as images and text. However, multimodal data inherently suffers from semantic inconsistency, which can introduce information conflicts or noise. Moreover, users' reliance on different modalities varies dynamically with context and time (multimodal dynamic preferences). These challenges may lead to truth deviation and deep semantic mismatch, ultimately degrading recommendation performance. To address these issues, we propose Dual-Path Multimodal Recommendation (DPRec), a novel model that improves precision and robustness of the recommendation through cross-modal information compensation and dynamic modal preference learning. Specifically, DPRec first employs a cross-modal attention mechanism to dynamically model inter-modal correlations, effectively exploring complementary and shared features for robust user and item representations. Second, it integrates feature projection, modality alignment, and dynamic weighting mechanisms to adaptively adjust modality importance based on user context, ensuring flexibility in handling preference dynamics. Lastly, a modality contrastive loss is utilized to maximize mutual information between modalities, mitigating semantic mismatch by enhancing deep collaborative representations. Extensive experiments on three public datasets show that DPRec consistently outperforms state-of-the-art (SOTA) methods, achieving average improvements of 3.94% in Recall@20 and 3.84% in NDCG@20. Our code is publicly available at: https://anonymous.4open.science/r/DPRec-4D15.