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DeHier: decoupled and hierarchical graph neural networks for multi-interest session-based recommendation

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Abstract

Session-based recommendation is a crucial task for learning the user’s intent and predicting their next clicks within a short session. Due to the diversity of user interests across their historical sessions, multi-interest session-based recommender systems have emerged accordingly. However, most existing methods suffer from random item embedding initialization and neglect interest propagation across sessions. In this paper, we propose a multi-interest session-based recommender system named DeHier, which consists of three parts: (1) decoupled item global embedding, (2) multi-interest item embedding, and (3) next-clicked item prediction. In the decoupled item global embedding module, DeHier learns coarse-grained item representation, subsequently capturing user long-term preferences. In the multi-interest item embedding module, DeHier employs hierarchical graph neural network (GNN) layers to propagate diversified user interests from historical sessions to current sessions and learn user short-term preferences. The user long-term preferences will be integrated into short-term preferences to make final recommendations. To demonstrate the effectiveness of DeHier, we conduct extensive experiments on three real-world datasets. The experimental results indicate that our proposed method is superior to the chosen baselines. Specifically, DeHier achieves up to 4.12% and 7.43% improvements of HR@5 and MRR@10, respectively, on the LastFM dataset.

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Availability of Data and Materials

In this paper, the experimental datasets are LastFM, Xing, and Reddit, which can be available at http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html, http://2016.recsyschallenge.com/, and https://www.kaggle.com/colemaclean/subreddit-interactions, respectively.

Notes

  1. http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html

  2. http://2016.recsyschallenge.com/

  3. https://www.kaggle.com/colemaclean/subreddit-interactions

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Funding

This work was partly supported by the National Key Research and Development Program of China (Research and Demonstration Application of Key Technologies for Personalized Learning Driven by Educational Big Data) under Grant 2023YFC3341200 and the Research Cultivation Fund for the Youth Teachers of South China Normal University under Grant 23KJ29.

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Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, 510631, Guangdong, China

    Ronghua Lin, Hao Zhong, Weisheng Li & Yong Tang

  2. School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, 511483, Guangdong, China

    Feiyi Tang

  3. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China

    Chengzhe Yuan

  4. Pazhou Lab, Guangzhou, 510330, Guangdong, China

    Ronghua Lin, Feiyi Tang, Chengzhe Yuan, Hao Zhong, Weisheng Li & Yong Tang

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  1. Ronghua Lin
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Contributions

R. L. and F. T. wrote the main manuscript text and proposed the method in the manuscript. C. Y. and H. Z. conducted the formal analysis and investigation. W. L. was devoted to data visualization in this manuscript. Y. T. reviewed the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ronghua Lin, Feiyi Tang or Weisheng Li.

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Lin, R., Tang, F., Yuan, C. et al. DeHier: decoupled and hierarchical graph neural networks for multi-interest session-based recommendation. World Wide Web 28, 1 (2025). https://doi.org/10.1007/s11280-024-01294-z

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