{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T18:29:05Z","timestamp":1780338545408,"version":"3.54.1"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:00:00Z","timestamp":1732924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62472108 and 62072122"],"award-info":[{"award-number":["62472108 and 62072122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Guangxi","award":["2023JJG170006"],"award-info":[{"award-number":["2023JJG170006"]}]},{"name":"Guangxi Bagui Youth Talent Program, the Project of Guangxi Key Laboratory of Eye Health","award":["GXYJK-202407"],"award-info":[{"award-number":["GXYJK-202407"]}]},{"name":"Guangxi Health Commission Eye and Related Diseases Artificial Intelligence Screen Technology Key Laboratory","award":["GXYAI-202402"],"award-info":[{"award-number":["GXYAI-202402"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>Increasing multiple behavior recommendation models have achieved great successes. However, many models do not consider commonalities and differences between behaviors and data sparsity of the target behavior. This article proposes a novel multi-behavior recommendation model based on contrastive clustering learning (MBRCC). Specifically, the graph convolutional network (GCN) is employed to obtain the embeddings of users and items, respectively. Then, three kinds of tasks (including behavior-level embedding, instance-level embedding, and cluster-level embedding) are designed to optimize the embeddings of users and items. In behavior-level embedding, we design an adaptive parameter learning strategy to analyze the impact of auxiliary behaviors on the target behavior. Then, the embeddings of users for each behavior are weighted to obtain the final embeddings of users. In instance-level embedding, we employ contrastive learning to analyze the instances of user and item for mitigating the issue of data sparsity. In cluster-level embedding, we design a new cluster contrastive learning method to capture the similarity between groups of user and item. Finally, we combine these three tasks to improve the quality of the embeddings of users and items. We conduct extensive experiments on three real-world datasets and experimental results indicate that the MBRCC remarkably outperforms numerous existing recommendation models.<\/jats:p>","DOI":"10.1145\/3698192","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T15:05:33Z","timestamp":1727795133000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":52,"title":["Contrastive Clustering Learning for Multi-Behavior Recommendation"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5839-7504","authenticated-orcid":false,"given":"Wei","family":"Lan","sequence":"first","affiliation":[{"name":"Guangxi University, Nanning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8113-0917","authenticated-orcid":false,"given":"Guoxian","family":"Zhou","sequence":"additional","affiliation":[{"name":"Guangxi University, Nanning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5506-8913","authenticated-orcid":false,"given":"Qingfeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangxi University, Nanning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8505-3342","authenticated-orcid":false,"given":"Wenguang","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangxi University, Nanning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0794-527X","authenticated-orcid":false,"given":"Shirui","family":"Pan","sequence":"additional","affiliation":[{"name":"Griffith University, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2766-3096","authenticated-orcid":false,"given":"Yi","family":"Pan","sequence":"additional","affiliation":[{"name":"Shenzhen University of Advanced Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4052-1823","authenticated-orcid":false,"given":"Shichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangxi Normal University, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,30]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331199"},{"key":"e_1_3_1_3_2","unstructured":"Rianne van den Berg Thomas N. Kipf and Max Welling. 2017. Graph convolutional matrix completion. Retrieved from https:\/\/www.kdd.org\/kdd2018\/files\/deep-learning-day\/DLDay18_paper_32.pdf"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3560487"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-023-01182-y"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16515"},{"issue":"2","key":"e_1_3_1_7_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3373807","article-title":"Efficient neural matrix factorization without sampling for recommendation","volume":"38","author":"Chen Chong","year":"2020","unstructured":"Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 2020. Efficient neural matrix factorization without sampling for recommendation. ACM Transactions on Information Systems 38, 2 (2020), 1\u201328.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5329"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570484"},{"key":"e_1_3_1_10_2","first-page":"1554","article-title":"Neural multi-task recommendation from multi-behavior data","author":"Gao Chen","year":"2019","unstructured":"Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, and Depeng Jin. 2019. Neural multi-task recommendation from multi-behavior data. In Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE \u201919). IEEE, 1554\u20131557.","journal-title":"Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE \u201919)"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3568022"},{"key":"e_1_3_1_12_2","first-page":"2052","volume-title":"International Joint Conference on Artificial Intelligence (IJCAI \u201922)","author":"Gu Shuyun","year":"2022","unstructured":"Shuyun Gu, Xiao Wang, Chuan Shi, and Ding Xiao. 2022. Self-supervised graph neural networks for multi-behavior recommendation. In International Joint Conference on Artificial Intelligence (IJCAI \u201922), 2052\u20132058."},{"key":"e_1_3_1_13_2","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30 (2017), 1025\u20131035.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911489"},{"key":"e_1_3_1_17_2","first-page":"1558","article-title":"Learning discrete representations via information maximizing self-augmented training","author":"Hu Weihua","year":"2017","unstructured":"Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama. 2017. Learning discrete representations via information maximizing self-augmented training. In International Conference on Machine Learning. PMLR, 1558\u20131567.","journal-title":"International Conference on Machine Learning"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401072"},{"issue":"1","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"bbab494","DOI":"10.1093\/bib\/bbab494","article-title":"KGANCDA: Predicting circRNA-disease associations based on knowledge graph attention network","volume":"23","author":"Lan Wei","year":"2022","unstructured":"Wei Lan, Yi Dong, Qingfeng Chen, Ruiqing Zheng, Jin Liu, Yi Pan, and Yi-Ping Phoebe Chen. 2022. KGANCDA: Predicting circRNA-disease associations based on knowledge graph attention network. Briefings in Bioinformatics 23, 1 (2022), bbab494.","journal-title":"Briefings in Bioinformatics"},{"key":"e_1_3_1_20_2","unstructured":"Wei Lan Guohang He Mingyang Liu Qingfeng Chen Junyue Cao and Wei Peng. 2024. Transformer-based single-cell language model: A survey. arXiv:2407.13205. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2407.13205"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2024.3387913"},{"key":"e_1_3_1_22_2","first-page":"1","volume-title":"Proceedings of the 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA \u201922)","author":"Lan Wei","year":"2022","unstructured":"Wei Lan and Wenguang Wang. 2022. Dual channel hybrid messaging-passing graph convolutional network for recommendation. In Proceedings of the 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA \u201922). IEEE, 1\u20137."},{"key":"e_1_3_1_23_2","first-page":"384","volume-title":"Neurocomputing","author":"Lan Wei","year":"2022","unstructured":"Wei Lan, Ximin Wu, Qingfeng Chen, Wei Peng, Jianxin Wang, and Yiping Phoebe Chen. 2022. GANLDA: Graph attention network for lncRNA-disease associations prediction. Neurocomputing 469 (2022), 384\u2013393."},{"issue":"8","key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"11382","DOI":"10.1109\/TNNLS.2023.3260258","article-title":"Multiview subspace clustering via low-rank symmetric affinity graph","volume":"35","author":"Lan Wei","year":"2023","unstructured":"Wei Lan, Tianchuan Yang, Qingfeng Chen, Shichao Zhang, Yi Dong, Huiyu Zhou, and Yi Pan. 2023. Multiview subspace clustering via low-rank symmetric affinity graph. IEEE Transactions on Neural Networks and Learning Systems 35, 8 (2023), 11382\u201311395.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"10","key":"e_1_3_1_25_2","first-page":"10857","article-title":"Meta auxiliary learning for top-k recommendation","volume":"35","author":"Li Ximing","year":"2022","unstructured":"Ximing Li, Chen Ma, Guozheng Li, Peng Xu, Chi Harold Liu, Ye Yuan, and Guoren Wang. 2022. Meta auxiliary learning for top-k recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 10 (2022), 10857\u201310870.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17037"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01639-z"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449986"},{"issue":"9","key":"e_1_3_1_29_2","first-page":"4385","article-title":"Modelling high-order social relations for item recommendation","volume":"34","author":"Liu Yang","year":"2020","unstructured":"Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, and Jie Tang. 2020. Modelling high-order social relations for item recommendation. IEEE Transactions on Knowledge and Data Engineering 34, 9 (2020), 4385\u20134397.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_30_2","unstructured":"Zhuang Liu Yunpu Ma Yuanxin Ouyang and Zhang Xiong. 2021. Contrastive learning for recommender system. arXiv:2101.01317. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2101.01317"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511019"},{"key":"e_1_3_1_32_2","unstructured":"Steffen Rendle Christoph Freudenthaler Zeno Gantner and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.1205.2618"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531927"},{"key":"e_1_3_1_34_2","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2017. Graph attention networks. arXiv:1710.10903. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.1710.10903"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380077"},{"issue":"3","key":"e_1_3_1_36_2","first-page":"2683","article-title":"Incorporating link prediction into multi-relational item graph modeling for session-based recommendation","volume":"35","author":"Wang Wen","year":"2021","unstructured":"Wen Wang, Wei Zhang, Shukai Liu, Qi Liu, Bo Zhang, Leyu Lin, and Hongyuan Zha. 2021. Incorporating link prediction into multi-relational item graph modeling for session-based recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 3 (2021), 2683\u20132696.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498527"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608807"},{"key":"e_1_3_1_40_2","first-page":"248","volume-title":"International Conference on Database Systems for Advanced Applications","author":"Wei Yunhe","year":"2022","unstructured":"Yunhe Wei, Huifang Ma, Yike Wang, Zhixin Li, and Liang Chang. 2022. Multi-behavior recommendation with two-level graph attentional networks. In International Conference on Database Systems for Advanced Applications. Springer, 248\u2013255."},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475665"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"key":"e_1_3_1_43_2","first-page":"166","volume-title":"International Conference on Database Systems for Advanced Applications","author":"Wu Yiqing","year":"2022","unstructured":"Yiqing Wu, Ruobing Xie, Yongchun Zhu, Xiang Ao, Xin Chen, Xu Zhang, Fuzhen Zhuang, Leyu Lin, and Qing He. 2022. Multi-view multi-behavior contrastive learning in recommendation. In International Conference on Database Systems for Advanced Applications. Springer, 166\u2013182."},{"key":"e_1_3_1_44_2","first-page":"05010","volume-title":"MATEC Web of Conferences","volume":"336","author":"Wu Ziteng","year":"2021","unstructured":"Ziteng Wu, Chengyun Song, Yunqing Chen, and Lingxuan Li. 2021. A review of recommendation system research based on bipartite graph. In MATEC Web of Conferences, Vol. 336, EDP Sciences, 05010."},{"key":"e_1_3_1_45_2","first-page":"1931","volume-title":"Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE \u201921)","author":"Xia Lianghao","year":"2021","unstructured":"Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Mengyin Lu, and Liefeng Bo. 2021. Multi-behavior enhanced recommendation with cross-interaction collaborative relation modeling. In Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE \u201921). IEEE, 1931\u20131936."},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16576"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462972"},{"key":"e_1_3_1_48_2","first-page":"1259","article-title":"Contrastive learning for sequential recommendation","author":"Xie Xu","year":"2022","unstructured":"Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE \u201922). IEEE, 1259\u20131273.","journal-title":"Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE \u201922)"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570386"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583361"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532009"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539342"},{"key":"e_1_3_1_53_2","first-page":"11129","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"37","author":"Zeng Dingyi","year":"2023","unstructured":"Dingyi Zeng, Wanlong Liu, Wenyu Chen, Li Zhou, Malu Zhang, and Hong Qu. 2023. Substructure aware graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 11129\u201311137."},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583530"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.11.101"},{"issue":"7","key":"e_1_3_1_56_2","first-page":"7382","article-title":"Reachable distance function for KNN classification","volume":"35","author":"Zhang Shichao","year":"2022","unstructured":"Shichao Zhang, Jiaye Li, and Yangding Li. 2022. Reachable distance function for KNN classification. IEEE Transactions on Knowledge and Data Engineering 35, 7 (2022), 7382\u20137396.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412119"},{"issue":"8","key":"e_1_3_1_58_2","first-page":"7980","article-title":"Finding high-quality item attributes for recommendation","volume":"35","author":"Zheng Xiaolin","year":"2022","unstructured":"Xiaolin Zheng, Yanchao Tan, Yan Wang, Xiangyu Wei, Shengjia Zhang, Chaochao Chen, Longfei Li, and Carl Yang. 2022. Finding high-quality item attributes for recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 8 (2022), 7980\u20137993.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00909"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.352"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698192","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3698192","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:18Z","timestamp":1750295838000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,30]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1,31]]}},"alternative-id":["10.1145\/3698192"],"URL":"https:\/\/doi.org\/10.1145\/3698192","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,30]]},"assertion":[{"value":"2024-02-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}