TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content
<|reference_start|>TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content: Sequential recommender systems explore users' preferences and behavioral patterns from their historically generated data. Recently, researchers aim to improve sequential recommendation by utilizing massive user-generated multi-modal content, such as reviews, images, etc. This content often contains inevitable noise. Some studies attempt to reduce noise interference by suppressing cross-modal inconsistent information. However, they could potentially constrain the capturing of personalized user preferences. In addition, it is almost impossible to entirely eliminate noise in diverse user-generated multi-modal content. To solve these problems, we propose a trustworthy sequential recommendation method via noisy user-generated multi-modal content. Specifically, we explicitly capture the consistency and complementarity of user-generated multi-modal content to mitigate noise interference. We also achieve the modeling of the user's multi-modal sequential preferences. In addition, we design a trustworthy decision mechanism that integrates subjective user perspective and objective item perspective to dynamically evaluate the uncertainty of prediction results. Experimental evaluation on four widely-used datasets demonstrates the superior performance of our model compared to state-of-the-art methods. The code is released at https://github.com/FairyMeng/TrustSR.<|reference_end|>
@article{yan2024truthsr:,
    title={TruthSR: Trustworthy Sequential Recommender Systems via User-generated
  Multimodal Content},
    author={Meng Yan, Haibin Huang, Ying Liu, Juan Zhao, Xiyue Gao, Cai Xu, Ziyu
  Guan, Wei Zhao},
    journal={arXiv preprint arXiv:2404.17238},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17238},
    primaryClass={cs.IR}
}