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Implicit relation-aware social recommendation with variational auto-encoder

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Abstract

Integrating social networks as auxiliary information shows effectiveness in improving the performance for a recommendation task. Typical models usually characterize the user trust relationship as a binary adjacent matrix derived from a social graph, which basically only incorporates neighborhood interactions, and then encodes the trust values of different individuals with the same value. Such methods fail to capture the implicit high-order relations hidden under a graph structure, and thereby ignore the impact of indirect influencers. To address the aforementioned problems, we present an I mplicit T rust R elation-A ware model (ITRA) based on Variational Auto-Encoder (VAE). ITRA adopts an attention module to feed the weighted trust embedding information into an inherited non-linear VAE structure. In this sense, ITRA could provide recommendations by reconstructing a non-binary adjacency social matrix with implicit high-order interactions from both indirect key opinion leaders and explicit connections from neighbors. The extensive experiments conducted on three datasets illustrate that ITRA could achieve a better performance compared to the state-of-the-art methods.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Qiqi Zheng, An Liu, Zhixu Li & Lei Zhao

  2. Department of Computing, Macquarie University, NSW, Australia

    Guanfeng Liu

  3. University of Electronic Science and Technology of China, Chengdu, China

    Kai Zheng

  4. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hongkong, China

    Xiaofang Zhou

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  1. Qiqi Zheng
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  2. Guanfeng Liu
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  3. An Liu
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  4. Zhixu Li
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  5. Kai Zheng
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  6. Lei Zhao
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  7. Xiaofang Zhou
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Correspondence to Guanfeng Liu.

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Zheng, Q., Liu, G., Liu, A. et al. Implicit relation-aware social recommendation with variational auto-encoder. World Wide Web 24, 1395–1410 (2021). https://doi.org/10.1007/s11280-021-00896-1

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