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AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems

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Abstract

As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and explainable recommendations. When it comes to interactions between entities, however, the majority of existing work fails to incorporate high-order relations that ensure recommendation accuracy. This paper proposes attention-enhanced joint knowledge and user preference propagation (AKUPP), which integrates two types of knowledge propagation. The first is propagating user preferences based on the users' history of interacting items through ripple sets. The second propagation employs an attention mechanism to emphasize the important semantics of relations, and with multiple layers, high-order relations are explored. Therefore, we successfully incorporate both side information and high-order relations in the knowledge graph. We show, via extensive experimentation on real-world datasets, that our approach outperforms numerous state-of-the-art baselines in terms of performance and accuracy.

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Notes

  1. Codes are available at https://github.com/helenma27/AKUPP/.

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Acknowledgements

We appreciate the comments from anonymous reviewers which will help further improve our work. This study has been partially supported by National Natural Science Foundation of China (61872164) and Program of Science and Technology Development Plan of Jilin Province of China (20190302032GX).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    **ntao Ma, Liyan Dong, Yuequn Wang & Hao Zhang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    **ntao Ma, Liyan Dong, Yuequn Wang & Hao Zhang

  3. School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China

    Yongli Li

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  1. **ntao Ma
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  2. Liyan Dong
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  5. Hao Zhang
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Correspondence to Hao Zhang.

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Ma, X., Dong, L., Wang, Y. et al. AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems. Knowl Inf Syst 65, 163–182 (2023). https://doi.org/10.1007/s10115-022-01693-6

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