2024/07/07 by Xinglong Wu, Ansheng Huang, Wu, Xinglong +9 · 1 citation
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Multimodal Machine Learning Applications #Recommender Systems and Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2407.05420
openalex publication_date 2024/07/07 · openalex created_date 2024/07/10 · openalex updated_date 2026/07/28
Multi-modal recommendation greatly enhances the performance of recommender systems by modeling the auxiliary information from multi-modality contents. Most existing multi-modal recommendation models primarily exploit multimedia information propagation processes to enrich item representations and directly utilize modal-specific embedding vectors independently obtained from upstream pre-trained models. However, this might be inappropriate since the abundant task-specific semantics remain unexplored, and the cross-modality semantic gap hinders the recommendation performance. Inspired by the recent progress of the cross-modal alignment model CLIP, in this paper, we propose a novel CLIP Enhanced Recommender (CLIPER) framework to bridge the semantic gap between modalities and extract fine-grained multi-view semantic information. Specifically, we introduce a multi-view modality-alignment approach for representation extraction and measure the semantic similarity between modalities. Furthermore, we integrate the multi-view multimedia representations into downstream recommendation models. Extensive experiments conducted on three public datasets demonstrate the consistent superiority of our model over state-of-the-art multi-modal recommendation models.