Towards Bridging the Cross-modal Semantic Gap for Multi-modal Recommendation

TL;DR

This paper introduces CLIPER, a multi-modal recommendation framework that leverages cross-modal alignment to bridge semantic gaps and improve recommendation accuracy by extracting fine-grained semantic information.

Contribution

The paper proposes a novel multi-view modality-alignment approach using CLIP to better capture cross-modal semantics for recommendation tasks.

Findings

CLIPER outperforms existing models on three public datasets.

The multi-view alignment improves semantic representation quality.

The approach effectively bridges the semantic gap across modalities.

Abstract

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 \textbf{CLIP} \textbf{E}nhanced \textbf{R}ecommender (\textbf{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.