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KAT: knowledge-aware attentive recommendation model integrating two-terminal neighbor features

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Abstract

Due to its ability to effectively address the cold start and sparsity problems in collaborative filtering, knowledge graph is commonly used as auxiliary information in recommendation systems. However, the existing recommendation algorithms based on knowledge graphs mainly focus on utilizing the connection structure to obtain user interests or item features, without emphasizing the simultaneous feature extraction on both the user and item sides. Therefore, the learned embeddings can not effectively represent the potential semantics of users and items. In this paper, we proposed KAT, a knowledge-aware attentive recommendation model integrating two-terminal neighbor features, which to extract fine-grained user and item features by alternating preference propagation and neighborhood information aggregation. The two modules automatically update and share entity embedding. Specifically, we introduce knowledge-aware attention mechanism to enhance the distinction of adjacent entities. Furthermore, we design a neighbor sampling mechanism to calculate the maximum node influence by extracting the largest connected subnet, which avoids the instability of the model performance caused by random sampling. We validate the effectiveness of KAT on four different datasets: movie, music, book, and grape (the latter is a dataset that we constructed through market research). Numerous experiments have demonstrated that KAT significantly outperforms several recent baselines, and AUC and ACC have increased by 2.81% and 1.28% respectively on our self-built dataset.

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Data availability

The datasets generated and analysed during the current study are available in the MovieLens, Last.FM, Book-Crossing repository, https://grouplens.org/datasets/movielens/, https://grouplens.org/datasets/hetrec-2011/, and http://www2.informatik.uni-freiburg.de/~cziegler/BX/. Dataset Grape is not publicly available because it is built through our own research.

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Acknowledgements

This work was supported by the China Agricultural Research System of MOF and MARA (CARS-29), and the open funds of the Key Laboratory of Viticulture and Enology, Ministry of Agriculture and Rural Affairs, PR China.

Funding

This work was supported by the China Agricultural Research System of MOF and MARA(CARS-29), and the open funds of the Key Laboratory of Viticulture and Enology, Ministry of Agriculture and Rural Affairs, PR China.

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Author notes

  1. Tianqi Liu, Xinxin Zhang and Wenzheng Wang contributed equally to this work.

Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Tsinghua East Road, Beijing, 100083, China

    Tianqi Liu, Xinxin Zhang, Wenzheng Wang & Weisong Mu

  2. The Key Laboratory of Viticulture and Enology, Ministry of Agriculture and Rural Affairs, Tsinghua East Road, Beijing, 100083, China

    Weisong Mu

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tianqi Liu, Xinxin Zhang Wenzheng Wang and Weisong Mu. The first draft of the manuscript was written by Tianqi Liu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Weisong Mu.

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All authors have read the final manuscript and approved their submission to International Journal of Machine Learning and Cybernetics. They certify that they have participated sufficiently in the work to take public responsibility for the appropriateness of the experimental design and method, and the collection, analysis, and interpretation of the data. The authors declare that they have no conflict of interest.

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Liu, T., Zhang, X., Wang, W. et al. KAT: knowledge-aware attentive recommendation model integrating two-terminal neighbor features. Int. J. Mach. Learn. & Cyber. 15, 4941–4958 (2024). https://doi.org/10.1007/s13042-024-02194-4

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