  Multi-modal recommender systems (MRSs) are pivotal in diverse online web
platforms and have garnered considerable attention in recent years. However,
previous studies overlook the challenges of (1) noisy multi-modal content, (2)
noisy user feedback, and (3) aligning multi-modal content with user feedback.
In order to tackle these challenges, we propose Denoising and Aligning
Multi-modal Recommender System (DA-MRS). To mitigate multi-modal noise, DA-MRS
first constructs item-item graphs determined by consistent content similarity
across modalities. To denoise user feedback, DA-MRS associates the probability
of observed feedback with multi-modal content and devises a denoised BPR loss.
Furthermore, DA-MRS implements Alignment guided by User preference to enhance
task-specific item representation and Alignment guided by graded Item relations
to provide finer-grained alignment. Extensive experiments verify that DA-MRS is
a plug-and-play framework and achieves significant and consistent improvements
across various datasets, backbone models, and noisy scenarios.
