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Deep adversarial group recommendation with user feature space separation

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Abstract

Many online services allow users to participate in various group activities such as online meeting or group buying and thus need to provide user groups with services that they are interested. The group recommender systems emerge as required and provide personalized services for various online user groups. Data sparsity is an important issue in group recommender systems, since even fewer group-item interactions are observed. Transfer learning has been one efficient tool to alleviate the data sparsity issue in recommender systems for individual users, but have not been utilized for group recommendation. Moreover, the group and the group members have complex and mutual relationships with each other, which exacerbates the difficulty in modelling the preferences of both a group and its members for recommendation. Therefore, group recommender systems face three main challenges that may significantly impact its quality and accuracy: (1) taking consideration of group member relationship and their interactions in modelling user and group preferences; (2) ensuring latent feature spaces between the users and groups are maximally matched; and (3) constructing a deep group recommendation method that both the individual user and group domains can benefit from a knowledge exchange. Hence, in this paper, we propose a deep adversarial group recommendation method, called DA-GR. User feature are separated into two subspaces to ensure only consistent group members’ feature knowledge can be extracted and shared with group preference modelling. Adversarial learning is used to effectively transfer consistent knowledge from individual user interactions to the group interaction domain through the bridge of group-user relationships. Extensive experiments, which demonstrate the effectiveness and superiority of our proposal, providing accurate recommendation for both individual users and groups, are conducted on public datasets. The source code of DA-GR is in https://github.com/ccnu-mathits/DA-GR.

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Notes

  1. https://github.com/caoda0721/SoAGREE

  2. https://recsys.acm.org/recsys11/camra/

  3. http://www.mafengwo.cn

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Acknowledgements

This work was jointly supported by the National Key R &D Program of China (2020AAA0108804), National Natural Science Foundation of China (62207017, 62107017, 62077021, 61937001), China Postdoctoral Science Foundation (2022M711282, 2020M682454), Hubei Provincial Natural Science Foundation of China (2022CFB414), Knowledge Innovation Program of Wuhan−Shuguang Project (2022010801020287) and Fundamental Research Funds for the Central Universities (CCNU22XJ033, CCNU22LJ005).

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Authors and Affiliations

  1. National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, 430079, China

    Jianwen Sun, Shangheng Du, Ruxia Liang, Xiaoxuan Shen, Qing Li, Sannyuya Liu & Zongkai Yang

  2. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China

    Jianwen Sun, Shangheng Du, Ruxia Liang, Xiaoxuan Shen, Qing Li, Sannyuya Liu & Zongkai Yang

  3. National Engineering Research Center for E-learning, Central China Normal University, Wuhan, 430079, China

    Sannyuya Liu & Zongkai Yang

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  1. Jianwen Sun
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Contributions

Jianwen Sun designed the proposed method and reviewed the whole manuscript; Shangheng Du implemented the algorithm and conducted experiments; Ruxia Liang conducted experiments and wrote the main manuscript text; Xiaoxuan Shen revised the manuscript and prepared figures 2-4; and all the other authors participated in reviewing the manuscript.

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Correspondence to Ruxia Liang.

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Sun, J., Du, S., Liang, R. et al. Deep adversarial group recommendation with user feature space separation. User Model User-Adap Inter (2023). https://doi.org/10.1007/s11257-023-09367-w

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