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Personalized recommendation with knowledge graph via dual-autoencoder

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Abstract

In the past decades, personalized recommendation systems have attracted a vast amount of attention and researches from multiple disciplines. Recently, for the powerful ability of feature representation learning, deep neural networks have achieved sound performance in the recommendation. However, most of the existing deep recommendation approaches require a large number of labeled data, which is often expensive and labor-some in applications. Meanwhile, the side information of users and items that can extend the feature space effectively is usually scarce. To address these problems, we propose a Personalized Recommendation method, which extends items’ feature representations with Knowledge Graph via dual-autoencoder (short for PRKG). More specifically, we first extract items’ side information from open knowledge graph like DBpedia as items’ feature extension. Secondly, we learn the low-dimensional representations of additional features collected from DBpedia via the autoencoder module and then integrate the processed features into the original feature space. Finally, the reconstructed features is incorporated into the semi-autoencoder for personalized recommendations. Extensive experiments conducted on several real-world datasets validate the effectiveness of our proposed methods compared to several state-of-the-art models.

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Notes

  1. DBpedia : https://wiki.dbpedia.org/.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under grants 61906060.

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Authors and Affiliations

  1. School of Information Engineering, Yangzhou University, Yangzhou, 225009, China

    Yang Yang, Yi Zhu & Yun Li

  2. Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Ministry of Education, Hefei, 230009, China

    Yi Zhu

  3. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Yi Zhu

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  1. Yang Yang
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  2. Yi Zhu
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Correspondence to Yun Li.

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Yang, Y., Zhu, Y. & Li, Y. Personalized recommendation with knowledge graph via dual-autoencoder. Appl Intell 52, 6196–6207 (2022). https://doi.org/10.1007/s10489-021-02647-1

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