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HIN-based rating prediction in recommender systems via GCN and meta-learning

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Abstract

Rating prediction is a crucial task for recommender systems, but it has the problem of difficulty in quickly capturing user preference transfer and cold-start problem. Thus, this paper proposes the meta-learning-based rating prediction model for heterogeneous information networks (HIN) called Meta-HRP (HIN-based Rating Prediction) to solve these problems. The model first constructs meta-tasks through meta-paths on HIN and then constructs an embedding representation generator based on graph convolutional network (GCN) and attention mechanism to generate embeddings for users and items. Then the proposed rating prediction meta-learner leverages historical interaction data to learn prior knowledge and rapidly adapts to new items based on a few recent user rating records to timely capture user preference transfer and alleviate the cold-start problem. We validate Meta-HRP with extensive experiments, and the proposed model reduces root mean square error by at least 8.49\(\%\) on average over the baselines on two public benchmark datasets. Furthermore, Meta-HRP outperforms the state-of-the-arts in most cold-start cases.

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Data Availability

The MovieLens and Douban Book datasets are available on a GitHub repository, https://github.com/librahu/HIN-Datasets-for-Recommendation-and-Network-Embedding.

Notes

  1. https://grouplens.org/datasets/movielens/

  2. https://book.douban.com

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62172065 and the Natural Science Foundation of Chongqing under Grant cstc2020jcyj-msxmX0137.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62172065 and the Natural Science Foundation of Chongqing under Grant cstc2020jcyj-msxmX0137.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Mingqiang Zhou, Kunpeng Li, Kailang Dai & Quanwang Wu

  2. Chongqing Key Laboratory of Software Theory and Technolog, Chongqing, 400044, China

    Mingqiang Zhou, Kunpeng Li, Kailang Dai & Quanwang Wu

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Contributions

Mingqiang Zhou and Kunpeng Li proposed the model in this paper and completed the experiment design and paper writing together. Kailang Dai was responsible for collecting datasets and drawing relevant charts in the paper. Quanwang Wu gave valuable suggestions and strong support for improving the method in this paper. Finally, all authors read the paper entirely.

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Correspondence to Mingqiang Zhou.

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Kunpeng Li, Kailang Dai and Quanwang Wu are contributed equally to this work.

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Zhou, M., Li, K., Dai, K. et al. HIN-based rating prediction in recommender systems via GCN and meta-learning. Appl Intell 53, 23271–23286 (2023). https://doi.org/10.1007/s10489-023-04769-0

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