Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
<|reference_start|>Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback: 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.<|reference_end|>
@article{xv2024improving,
    title={Improving Multi-modal Recommender Systems by Denoising and Aligning
  Multi-modal Content and User Feedback},
    author={Guipeng Xv, Xinyu Li, Ruobing Xie, Chen Lin, Chong Liu, Feng Xia,
  Zhanhui Kang, Leyu Lin},
    journal={arXiv preprint arXiv:2406.12501},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12501},
    primaryClass={cs.IR}
}