TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content

• URL: http://arxiv.org/abs/2404.17238v1
• Date: Fri, 26 Apr 2024 08:23:36 GMT
• Title: TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content
• Authors: Meng Yan, Haibin Huang, Ying Liu, Juan Zhao, Xiyue Gao, Cai Xu, Ziyu Guan, Wei Zhao,
• Abstract summary: We propose a trustworthy sequential recommendation method via noisy user-generated multi-modal content. Specifically, we capture the consistency and complementarity of user-generated multi-modal content to mitigate noise interference. In addition, we design a trustworthy decision mechanism that integrates subjective user perspective and objective item perspective.
• Score: 21.90660366765994
• License:
• Abstract: 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.

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