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McHa: a multistage clustering-based hierarchical attention model for knowledge graph-aware recommendation

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Abstract

Knowledge graph-aware recommendation has become an important research topic in recent years. The user preference representation, which preserves the user’s taste towards items (e.g., movies, books.), is obtained through aggregating the information of entities or attributes in knowledge graphs directly. However, the fine-grained heterogeneity information, which can be derived from the groups of items or entities, remains barely exploited in the process of encoding the user interaction intention for the items. To fill up this gap, we propose a Multistage Clustering-based Hierarchical Attention (McHa) model to capture the user preference representation. In our work, we first group the items and their neighboring entities in the knowledge graph into item clusters and entity clusters (jointly referred to as multistage clusters), respectively. Then, the user preference representation is obtained by hierarchically aggregating the heterogeneity information derived from the multistage clusters with the weights generated by the hierarchical attention layers. We conduct extensive experimental comparisons with baselines and the variants. The experimental results indicate that McHa has achieved state-of-the-art performance on three benchmark datasets in two scenarios.

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Notes

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

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

  3. http://www2.informatik.uni-freiburg.de/~cziegler/BX/

  4. https://searchengineland.com/library/bing/bing-satori

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Acknowledgements

This work was supported by the National Key Research and Development Plan of China (No.2018YFB1003804) and the Project of State Grid Shandong Electric Power Company (2020A-135).

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, China

    Jihu Wang, Yuliang Shi, Zhiyong Chen & Hui Li

  2. Dareway Software Co., Ltd, Jinan, China

    Yuliang Shi

  3. Information&Telecommunications Company, State Grid Shandong Electric Power Company, Jinan, China

    Dong Li

  4. School of Information Science and Engineering, University of Jinan, Jinan, China

    Kun Zhang

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  1. Jihu Wang
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Wang, J., Shi, Y., Li, D. et al. McHa: a multistage clustering-based hierarchical attention model for knowledge graph-aware recommendation. World Wide Web 25, 1103–1127 (2022). https://doi.org/10.1007/s11280-022-01022-5

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