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A Survey of Recommender Systems Based on Hypergraph Neural Networks

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Smart Computing and Communication (SmartCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13828))

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Abstract

Unlike highly purposeful search, a recommender system tends to uncover the user’s potential interests and is a personalized information filtering system. Recently, the performance of hypergraph neural networks in classification tasks has attracted much attention. Compared with traditional recommender systems, hypergraph neural network-based recommender systems have better mining higher-order associations, accurate modeling of multivariate relationships, handling of multimodal and heterogeneous data, and clustering advantages. This fact drives the development of recommendation algorithms based on hypergraph neural networks. To this end, we 1) define generic links of recommender systems, and systematically analyze the challenges of hypergraph neural network-based recommender systems in different research directions. 2) present some new perspectives on existing weaknesses and future developments.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Canwei Liu, Tingqin He, Hangyu Zhu & Yanlu Li

  2. Guangdong Financial High-tech Zone “Blockchain +” Fintech Research Institute, Guangzhou, 510623, China

    Hangyu Zhu

  3. Hunan Key Laboratory for Service computing and Novel Software Technology, Xiangtan, 411201, China

    Canwei Liu, Tingqin He, Hangyu Zhu & Yanlu Li

  4. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Songyou Xie

  5. Higher Colleges of Technology, Abu Dhabi, United Arab Emirates

    Osama Hosam

Authors
  1. Canwei Liu
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  2. Tingqin He
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  3. Hangyu Zhu
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  4. Yanlu Li
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  5. Songyou Xie
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  6. Osama Hosam
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Corresponding author

Correspondence to Tingqin He .

Editor information

Editors and Affiliations

  1. Dakota State University, Madison, SD, USA

    Meikang Qiu

  2. Fudan University, Shanghai, China

    Zhihui Lu

  3. Ibaraki University, Ibaraki, Japan

    Cheng Zhang

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Liu, C., He, T., Zhu, H., Li, Y., Xie, S., Hosam, O. (2023). A Survey of Recommender Systems Based on Hypergraph Neural Networks. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_10

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  • DOI: https://doi.org/10.1007/978-3-031-28124-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28123-5

  • Online ISBN: 978-3-031-28124-2

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