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Intra- and inter-association attention network-enhanced policy learning for social group recommendation

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Abstract

Social Group Recommendation (SGR) is a critical task to recommend items to a group of users in social network platforms, such as Meetup, Douban, Mofengwo, etc. Recently, many state-of-the-art works have addressed the group decision making with pre-defined aggregation strategies or neural-based methods. The main challenge is how to capture the intra-interaction and inter-association among users, groups, and items. In term of this issue, we propose an Intra- and inter-association attention network with Policy learning for Social Group Recommendation (IP-SGR). Specifically, for intra-interaction attention model, we capture the preference of user pair agreement with the representation of their co-interaction items, while a gate filtering component is utilized to aggregate the group agreement with the member representations of the group. In addition, to capture the inter-association representation of groups and items, we present inter-group attention network and inter-item prototype learning model, respectively. Finally, we propose a reinforcement learning-based model to obtain the positive and negative reward for social group recommendation. Extensive experiments on three real-world datasets demonstrate our proposed IP-SGR model significantly outperforms several state-of-the-art methods in terms of HR and NDCG.

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Notes

  1. https://github.com/LianHaiMiao/Attentive-Group-Recommendation/tree/master/data

  2. https://sites.google.com/view/hongzhi-yin/datasets

  3. https://www.yelp.com/dataset/challenge

  4. https://github.com/caoda0721/SoAGREE/tree/master/data/MaFengWo

  5. http://www.pytorch.org

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant Nos. 72172057, 92046026, 71701089, in part by the the Fundamental Research on Advanced Leading Technology Project of Jiangsu Province under Grant BK20192004C, BK20202011, the Jiangsu Provincial Key Research and Development Program under grant BE2020001-3, and the National Center for International Joint Research on E-Business Information Processing under Grant 2013B01035.

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Authors and Affiliations

  1. Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, 210046, Nanjing, China

    Youquan Wang, Zhiwen Dai, Haicheng Tao & Guixiang Zhu

  2. School of Computer and Information Engineering, Hohai University, 211100, Nanjing, China

    Jie Cao

  3. Department of Computing, Macquarie University, 2109, Sydney, Australia

    Jia Wu

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  1. Youquan Wang
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Correspondence to Jie Cao.

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This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications

Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu

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Wang, Y., Dai, Z., Cao, J. et al. Intra- and inter-association attention network-enhanced policy learning for social group recommendation. World Wide Web 26, 71–94 (2023). https://doi.org/10.1007/s11280-022-01035-0

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