  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 .
