DiffMM: Multi-Modal Diffusion Model for Recommendation

TL;DR

DiffMM introduces a multi-modal graph diffusion model with contrastive learning to enhance user representations in recommendation systems, effectively addressing data sparsity and improving multi-modal alignment.

Contribution

The paper proposes a novel DiffMM framework that combines graph diffusion and contrastive learning to better integrate multi-modal data in recommendation systems.

Findings

Outperforms baseline models on three public datasets

Effectively aligns multi-modal features with user-item interactions

Enhances user representation learning in sparse data scenarios

Abstract

The rise of online multi-modal sharing platforms like TikTok and YouTube has enabled personalized recommender systems to incorporate multiple modalities (such as visual, textual, and acoustic) into user representations. However, addressing the challenge of data sparsity in these systems remains a key issue. To address this limitation, recent research has introduced self-supervised learning techniques to enhance recommender systems. However, these methods often rely on simplistic random augmentation or intuitive cross-view information, which can introduce irrelevant noise and fail to accurately align the multi-modal context with user-item interaction modeling. To fill this research gap, we propose a novel multi-modal graph diffusion model for recommendation called DiffMM. Our framework integrates a modality-aware graph diffusion model with a cross-modal contrastive learning paradigm to improve modality-aware user representation learning. This integration facilitates better alignment between multi-modal feature information and collaborative relation modeling. Our approach leverages diffusion models' generative capabilities to automatically generate a user-item graph that is aware of different modalities, facilitating the incorporation of useful multi-modal knowledge in modeling user-item interactions. We conduct extensive experiments on three public datasets, consistently demonstrating the superiority of our DiffMM over various competitive baselines. For open-sourced model implementation details, you can access the source codes of our proposed framework at: https://github.com/HKUDS/DiffMM .