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Personalized recommendation system based on knowledge embedding and historical behavior

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Abstract

Collaborative filtering (CF) usually suffers from limited performance in recommendation systems due to the sparsity of user–item interactions and cold start problems. To address these issues, auxiliary information from knowledge graphs, such as social networks and item properties, is typically used to boost performance. The current recommended algorithms based on knowledge graphs fail to utilize rich semantic associations. In this paper, we regard knowledge graphs as heterogeneous networks to add auxiliary information, propose a recommendation system with unified embeddings of behavior and knowledge features, and mine user preferences from their historical behavior and knowledge graphs to provide more accurate and diverse recommendations to the users. Our proposed ReBKC shows a significant improvement on three datasets compared to state-of-the-art methods. These results verify the effectiveness of learning short-term and long-term user preferences from their historical behavior and by integrating knowledge graphs to deeply identify user preferences.

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Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2018YFC0807500), by National Natural Science Foundation of China (No. U19A2059), and by Ministry of Science and Technology of Sichuan Province Program (No. 2018GZDZX0048,20ZDYF0343).

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Authors and Affiliations

  1. Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Bei Hui & Lizong Zhang

  2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xue Zhou & Yuhui Nian

  3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xiao Wen

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  1. Bei Hui
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  2. Lizong Zhang
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  3. Xue Zhou
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Correspondence to Lizong Zhang.

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All authors declare that: (i) no support, financial or otherwise, has been received from any organization that may have an interest in the submitted work ; and (ii) there are no other relationships or activities that could appear to have influenced the submitted work.

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Hui, B., Zhang, L., Zhou, X. et al. Personalized recommendation system based on knowledge embedding and historical behavior. Appl Intell 52, 954–966 (2022). https://doi.org/10.1007/s10489-021-02363-w

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