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Meta-path automatically extracted from heterogeneous information network for recommendation

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Abstract

Heterogeneous information networks have been proven to effectively improve recommendations due to their diverse information content. However, there are still two challenges for recommendation methods based on heterogeneous information networks. To begin with, current methods often depend on experts to manually craft meta-paths, and it can be challenging to define an adequate set of meta-paths for complex task scenarios. Second, most models fail to fully explore user preferences for paths or meta-paths whileimultaneously learning path or meta-path explicit representations. To tackle the aforementioned issues, we propose a model for recommendation utilizing meta-path automatically extracted from heterogeneous information network, called MAERec. Specifically, MAERec employs an automatic approach to extract high-quality path instances from heterogeneous information networks and construct meta-paths. These meta-paths are then utilized by a hierarchical attention network to learn an explicit representation of the meta-path-based context. Extensive experiments conducted on various real-world datasets not only showcase the superior performance of MAERec when compared to state-of-the-art methods but also underscore its capability to automatically discover high-quality path instances for meta-path extraction.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. https://grouplens.org/datasets/movielens/100k/

  2. https://grouplens.org/datasets/hetrec-2011/

  3. http://www.yelp.com/dataset-challenge

  4. https://pytorch.org/

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Acknowledgements

The work is supported by the Natural Science Foundation of Chongqing (No. CSTB2023NSCQ-MSX0343), the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJZD-K202101105, KJQN202001136), Humanities and Social Sciences Research Program of Chongqing Municipal Education Commission (No.22SKGH302), the National Natural Science Foundation of China (No.61702063), the Action Plan for High-Quality Development of Graduate Education of Chongqing University of Technology (NO.gzlcx20233363).

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Authors and Affiliations

  1. School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400054, China

    Yihao Zhang, Weiwen Liao, Yulin Wang, Junlin Zhu, Ruizhen Chen & Yunjia Zhang

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  1. Yihao Zhang
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  2. Weiwen Liao
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  3. Yulin Wang
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  4. Junlin Zhu
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  5. Ruizhen Chen
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  6. Yunjia Zhang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Yihao Zhang and Weiwen Liao. The first draft of the manuscript was written by Weiwen Liao and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yihao Zhang.

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Zhang, Y., Liao, W., Wang, Y. et al. Meta-path automatically extracted from heterogeneous information network for recommendation. World Wide Web 27, 26 (2024). https://doi.org/10.1007/s11280-024-01265-4

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