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Path-enhanced explainable recommendation with knowledge graphs

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Abstract

Recommender systems, which are used to predict user requirements precisely, play a vital role in the modern internet industry. As an effective tool with rich semantics, knowledge graphs have recently attracted growing research attention in enhancing recommendation results. By mining multihop relations (i.e., paths) between user-item interactions within a knowledge graph, implicit user preferences and other side information can be clearly revealed. Nevertheless, existing knowledge graph-based recommendation methods have two fundamental limitations. First, the indiscriminate utilization of user-item path sets conveys unclear information and negatively influences explainability. Moreover, obtaining reliable recommendation results with these methods requires large amounts of prior knowledge, which indicates that they show poor performance in terms of accuracy and handling cold-start issues. To address these issues, we propose a novel model called the Path-enhanced Recurrent Network (PeRN). Specifically, PeRN integrates a recurrent neural network encoder with a metapath-based entropy encoder to increase explainability and accuracy and reduce cold-start costs. The recurrent network encoder has a strong ability to represent sequential path semantics in a knowledge graph, while the entropy encoder, as an efficient statistical analysis tool, leverages metapath information to differentiate paths in a single user-item interaction. A path extraction algorithm with a bidirectional scheme is also proposed to make PeRN more feasible. The experimental results on two real-world datasets demonstrate our significant improvements with reasonable explanations, promising accuracy and a minimal amount of prior knowledge compared with several state-of-the-art baselines.

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Notes

  1. https://www.kaggle.com/c/kkbox-music-recommendation-challenge

  2. https://www.kaggle.com/suchitgupta60/imdb-data

  3. https://grouplens.org/datasets/movielens/

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Acknowledgments

This work was supported in part by National Key R&D Program of China under Grants no. 2018YFB1404302, National Natural Science Foundation of China under Grants No.62072203.

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Authors and Affiliations

  1. National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Yafan Huang, Feng Zhao, Xiangyu Gui & Hai Jin

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  1. Yafan Huang
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  2. Feng Zhao
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  3. Xiangyu Gui
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  4. Hai Jin
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Correspondence to Feng Zhao.

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This article belongs to the Topical Collection: Special Issue on Explainability in the Web

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Huang, Y., Zhao, F., Gui, X. et al. Path-enhanced explainable recommendation with knowledge graphs. World Wide Web 24, 1769–1789 (2021). https://doi.org/10.1007/s11280-021-00912-4

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