Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendation

TL;DR

This paper introduces MuSiC, a novel cross-domain recommendation model that utilizes multimodal item features and side users to improve cold-start user recommendations across domains, achieving state-of-the-art results.

Contribution

The paper proposes a diffusion-based approach leveraging multimodal data and side users, addressing underutilization of data and side user neglect in cross-domain recommendation.

Findings

MuSiC outperforms baseline models on Amazon dataset

Effective use of multimodal features enhances recommendation accuracy

Incorporating side users improves cold-start user recommendations

Abstract

Cross-domain recommendation (CDR) aims to address the persistent cold-start problem in Recommender Systems. Current CDR research concentrates on transferring cold-start users' information from the auxiliary domain to the target domain. However, these systems face two main issues: the underutilization of multimodal data, which hinders effective cross-domain alignment, and the neglect of side users who interact solely within the target domain, leading to inadequate learning of the target domain's vector space distribution. To address these issues, we propose a model leveraging Multimodal data and Side users for diffusion Cross-domain recommendation (MuSiC). We first employ a multimodal large language model to extract item multimodal features and leverage a large language model to uncover user features using prompt learning without fine-tuning. Secondly, we propose the cross-domain diffusion module to learn the generation of feature vectors in the target domain. This approach involves learning feature distribution from side users and understanding the patterns in cross-domain transformation through overlapping users. Subsequently, the trained diffusion module is used to generate feature vectors for cold-start users in the target domain, enabling the completion of cross-domain recommendation tasks. Finally, our experimental evaluation of the Amazon dataset confirms that MuSiC achieves state-of-the-art performance, significantly outperforming all selected baselines. Our code is available: https://anonymous.4open.science/r/MuSiC-310A/.