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Leveraging the fine-grained user preferences with graph neural networks for recommendation

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Abstract

With the explosion of information, recommendation systems have become important for users to find their interested information. Existing recommendation methods mainly utilize user historical interaction with items or user ratings to capture user past preferences. However, there is ignorance of various personalized reasons for users preferring an item, in which the reasons always dominate users’ preference strengths on the item. In addition, the linear nature of traditional recommendation methods makes them less effective in dealing with complex data. With the development of deep learning methods, graph neural networks provide an unprecedented opportunity for recommendations, since the user-item interactions can be naturally represented as a graph and the method can extract high-order complex relationships between users and items. In this paper, we propose a novel method leveraging the FIne-Grained user preferences with Graph Neural Networks (FigGNN) for recommendation to tackle these issues. More specifically, user-item interactions with user annotated tags and user ratings are constructed as a graph. In the process of graph message propagation, the user annotated tags are incorporated for understanding user preference reasons on items, and heterogeneous user rating levels are utilized for recognizing user preference strengths on items. Experiments have been conducted on the MovieLens dataset and the results show a superior performance of FigGNN over baselines in terms of precision and recall, which demonstrates the effectiveness of the proposed method.

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Data availability

The data used in the paper can be available at https://grouplens.org/datasets/movielens/.

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Acknowledgements

We appreciate the National Natural Science Foundation of China, Science Fund for Distinguished Young Scholars of AnHui, Anhui Provincial Key Research and Development Program, and Fundamental Research Funds for the Central Universities for supporting this research.

Funding

This work is partially supported by the National Natural Science Foundation of China (72071062, 72071061), Science Fund for Distinguished Young Scholars of AnHui (2208085J12), Anhui Provincial Key Research and Development Program (202104a05020038), and Fundamental Research Funds for the Central Universities (PA2021KCPY0032).

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, Anhui, 230009, People’s Republic of China

    Gang Wang, Hanru Wang & Ying Yang

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making (Hefei University of Technology), Ministry of Education, Hefei, Anhui, People’s Republic of China

    Gang Wang

  3. Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei, 230009, China

    Gang Wang

  4. School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin, 300222, People’s Republic of China

    Jing Liu

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  1. Gang Wang
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All authors contributed to the study's conception and design. Material preparation, data collection and analysis, and experiments were performed by Gang Wang and Hanru Wang. The first draft of the manuscript was written by Hanru Wang and revised by Gang Wang. All authors read and approved the final manuscript.

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Correspondence to Gang Wang.

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Wang, G., Wang, H., Liu, J. et al. Leveraging the fine-grained user preferences with graph neural networks for recommendation. World Wide Web 26, 1371–1393 (2023). https://doi.org/10.1007/s11280-022-01099-y

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