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Group-based social diffusion in recommendation

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Abstract

In social-enhanced recommendation systems such as Twitter and Weibo, users could get information from both personalized recommendation and social diffusion modules. In real-world scenarios, the user-group-user based social diffusion plays an essential role to efficiently broadcast information to groups of target users. Through this diffusion path, users first click items provided by the recommendation module, and then share the clicked items to the target user groups. Other users in the group can click the shared items, and return back to the recommendation module for more contents and related items. However, most social-enhanced recommendation systems merely focus on the recommendation module that they can directly influence, ignoring explicitly modeling and predicting for the social diffusion module. In this work, we propose a novel Group-based social diffusion (GSD) model, which aims to jointly optimize the click, share, and return stages in social-enhanced recommendation. We design a heterogeneous ternary graph neural network to jointly model the complex binary and ternary relations among users, items, and groups. We conduct extensive experiments and achieve significant improvements on all click, share, and return prediction tasks, and also achieve promising results on a new full-chain social impact prediction task.

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Availability of Supporting Data

The dataset we use is a private dataset from our system. The basic information for this dataset is introduced in Section 4.1. The source code is in appendix files and can be published with this paper.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xumin Chen, Peng Cui, Ziwei Zhang & Shiqiang Yang

  2. WeChat, Tencent, Shenzhen, China

    Xumin Chen, Ruobing Xie, Zhijie Qiu, Shukai Liu, Bo Zhang & Leyu Lin

Authors
  1. Xumin Chen
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  2. Ruobing Xie
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  3. Zhijie Qiu
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  4. Peng Cui
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  5. Ziwei Zhang
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  6. Shukai Liu
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  7. Shiqiang Yang
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  8. Bo Zhang
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  9. Leyu Lin
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Contributions

Xumin Chen and Ruobing Xie: conceptualization, methodology and writing.

Xumin Chen: coding.

Zhijie Qiu, Peng Cui, Ziwei Zhang, Shukai Liu: discussion and revising.

Shiqiang Yang, Bo Zhang and Leyu Lin: supervision.

Corresponding author

Correspondence to Shiqiang Yang.

Ethics declarations

Ethics approval and consent to participate

We claim that there is no ethics issue in this work. The dataset has only user behavioral information occurred in our system and no other sensitive profiles. All information is collected after user approvals, and all data are processed via data mask-ing to protect user’s privacy.

Consent for Publication

All of the authors make substantial contributions, approve the version to be published and agree to be accountable for all aspects of the work. All of the authors draft the work or revise it critically for important intellectual content.

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Cite this article

Chen, X., Xie, R., Qiu, Z. et al. Group-based social diffusion in recommendation. World Wide Web 26, 1775–1792 (2023). https://doi.org/10.1007/s11280-022-01079-2

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  • DOI: https://doi.org/10.1007/s11280-022-01079-2

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