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Group-to-group recommendation with neural graph matching

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Abstract

Nowadays, with the development of recommender systems, an emerging recommendation scenario called group-to-group recommendation has played a vital role in information acquisition for users. The new recommendation scenario seeks to recommend a group of related items to users with similar interests. To some extent, it alleviates the problem of point-to-point recommendations getting trapped in an information cocoon due to an over-reliance on user behaviors. For the new recommendation scenario, the existing recommendation methods cannot model the complex interactions between user groups and item groups, thus affecting the accuracy of the group-to-group recommendation. In this paper, we propose a group-to-group recommendation method, which abstracts user groups and item groups into graphs and calculates the similarity between two graphs based on graph matching, dubbed as GMRec. Specifically, we construct the graph of user groups and item groups and then calculate the graph similarity scores between user groups and item groups from two perspectives of feature matching and structure matching. Experimental results show that our model achieves higher accuracy than state-of-the-art models on three industrial datasets with different group sizes, with a maximum improvement of 8.2%.

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We will indicate the source of the three industrial datasets and the company of the datasets after review.

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Funding

This work is supported in part by the National Natural Science Foundation of China (No. 62192784, U1936104, U20B2045, 62172052, 62002029).

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Authors and Affiliations

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Chunchen Wang, Cheng Yang & Chuan Shi

  2. WeChat Search Application Department, Tencent, Beijing, 100000, China

    Chunchen Wang, Wei Wang, Ruobing Xie, Yuanfu Lu, Haili Yang & Xu Zhang

Authors
  1. Chunchen Wang
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  2. Wei Wang
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  3. Cheng Yang
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  4. Chuan Shi
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  5. Ruobing Xie
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  6. Yuanfu Lu
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  7. Haili Yang
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  8. Xu Zhang
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Contributions

Chunchen Wang and Cheng Yang wrote the main manuscript text. Wei Wang and Ruobing Xie prepared figures and fixed text. All authors reviewed the manuscript.

Corresponding author

Correspondence to Chuan Shi.

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We collect three industrial datasets from real business scenarios, which is the biggest social platform serving more than one billion users, and users can browse and share articles. To safeguard user privacy, we have implemented data anonymization techniques and rigorous desensitization measures.

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The authors declare no competing interests.

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Wang, C., Wang, W., Yang, C. et al. Group-to-group recommendation with neural graph matching. World Wide Web 27, 19 (2024). https://doi.org/10.1007/s11280-024-01250-x

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  • DOI: https://doi.org/10.1007/s11280-024-01250-x

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