Self‐supervised graph learning for occasional group recommendation

International Journal of Intelligent SystemsVolume 37, Issue 12 p. 10880-10902 RESEARCH ARTICLE Self-supervised graph learning for occasional group recommendation Bowen Hao, Bowen Hao orcid.org/0000-0002-0546-9671 Capital Normal University, Beijing, ChinaSearch for more papers by this authorHongzhi Yin, Corresponding Author Hongzhi Yin [email protected] The University of Queensland, Brisbane, Australia Correspondence Hongzhi Yin, The University of Queensland, Australia. Email: [email protected]Search for more papers by this authorCuiping Li, Cuiping Li Renmin University of China, Beijing, ChinaSearch for more papers by this authorHong Chen, Hong Chen Renmin University of China, Beijing, ChinaSearch for more papers by this author Bowen Hao, Bowen Hao orcid.org/0000-0002-0546-9671 Capital Normal University, Beijing, ChinaSearch for more papers by this authorHongzhi Yin, Corresponding Author Hongzhi Yin [email protected] The University of Queensland, Brisbane, Australia Correspondence Hongzhi Yin, The University of Queensland, Australia. Email: [email protected]Search for more papers by this authorCuiping Li, Cuiping Li Renmin University of China, Beijing, ChinaSearch for more papers by this authorHong Chen, Hong Chen Renmin University of China, Beijing, ChinaSearch for more papers by this author First published: 22 August 2022 https://doi.org/10.1002/int.23011Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract As an important branch in Recommender System, occasional group recommendation has received more and more attention. In this scenario, each occasional group (cold-start group) has no or few historical interacted items. As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high-quality group representations. The recent proposed Graph Neural Networks (GNNs), which incorporate the high-order neighbors of the target occasional group, can alleviate the above problem in some extent. However, these GNNs still can not explicitly strengthen the embedding quality of the high-order neighbors with few interactions. Motivated by the self-supervised learning technique, which is able to find the correlations within the data itself, we propose a self-supervised graph learning framework, which takes the user/item/group embedding reconstruction as the pretext task to enhance the embeddings of the cold-start users/items/groups. To explicitly enhance the high-order cold-start neighbors' embedding quality, we further introduce an embedding enhancer, which leverages the self-attention mechanism to improve the embedding quality for them. Comprehensive experiments show the advantages of our proposed framework than the state-of-the-art methods. Open Research DATA AVAILABILITY STATEMENT We use the data set in this paper "Social Influence-based Group Representation Learning for Group Recommendation". REFERENCES 1Yang Q, Hu S, Zhang W, Zhang J. Attention mechanism and adaptive convolution actuated fusion network for next poi recommendation. Int J Intell. 2022; 1-21. doi:10.1002/int.22909 2Zhang X, Ma1 H, Gao Z, Li Z, Chang L. Exploiting cross-session information for knowledge-aware session-based recommendation via graph attention networks. Int J Intell. 2022. 3Yu X, Che X, Mao Q, Gong Z, Fu W, Zheng X. PF-ITS: intelligent traffic service recommendation based on DeepAFM model. 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