{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:01:11Z","timestamp":1775815271221,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,7,19]]},"DOI":"10.1145\/3539618.3591665","type":"proceedings-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:22:23Z","timestamp":1689726143000},"page":"1137-1146","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":123,"title":["Disentangled Contrastive Collaborative Filtering"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3332-1073","authenticated-orcid":false,"given":"Xubin","family":"Ren","sequence":"first","affiliation":[{"name":"University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0725-2211","authenticated-orcid":false,"given":"Lianghao","family":"Xia","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9770-7616","authenticated-orcid":false,"given":"Jiashu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Wilfrid Laurier University, Waterloo, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0684-6205","authenticated-orcid":false,"given":"Dawei","family":"Yin","sequence":"additional","affiliation":[{"name":"Baidu Inc, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-1512","authenticated-orcid":false,"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13660-016-0985-4"},{"key":"e_1_3_2_2_2_1","unstructured":"Xuheng Cai Chao Huang Lianghao Xia and Xubin Ren. 2023. LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. In ICLR."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Jianxin Chang Chen Gao Yu Zheng Yiqun Hui Yanan Niu Yang Song Depeng Jin and Yong Li. 2021. Sequential recommendation with graph neural networks. In SIGIR. 378--387.","DOI":"10.1145\/3404835.3462968"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Chong Chen Min Zhang Weizhi Ma Yiqun Liu and Shaoping Ma. 2020d. Jointly non-sampling learning for knowledge graph enhanced recommendation. In SIGIR. 189--198.","DOI":"10.1145\/3397271.3401040"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_2_6_1","first-page":"26924","article-title":"Curriculum Disentangled Recommendation with Noisy Multi-feedback","volume":"34","author":"Chen Hong","year":"2021","unstructured":"Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, and Wenwu Zhu. 2021a. Curriculum Disentangled Recommendation with Noisy Multi-feedback. NeurIPS, Vol. 34, 26924--26936.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Mengru Chen Chao Huang Lianghao Xia Wei Wei Yong Xu and Ronghua Luo. 2023. Heterogeneous Graph Contrastive Learning for Recommendation. In WSDM. 544--552.","DOI":"10.1145\/3539597.3570484"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Yongjun Chen Zhiwei Liu Jia Li Julian McAuley and Caiming Xiong. 2022. Intent contrastive learning for sequential recommendation. In WWW. 2172--2182.","DOI":"10.1145\/3485447.3512090"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Yudong Chen Xin Wang Miao Fan Jizhou Huang Shengwen Yang et al. 2021b. Curriculum meta-learning for next POI recommendation. In KDD. 2692--2702.","DOI":"10.1145\/3447548.3467132"},{"key":"e_1_3_2_2_11_1","volume-title":"NIPS (2020)","author":"Chen Yu","year":"2020","unstructured":"Yu Chen, Lingfei Wu, and Mohammed Zaki. 2020b. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. NIPS (2020), 19314--19326."},{"key":"e_1_3_2_2_12_1","first-page":"1","article-title":"Bounds on the Jensen gap, and implications for mean-concentrated distributions","volume":"16","author":"Gao Xiang","year":"2019","unstructured":"Xiang Gao, Meera Sitharam, and Adrian E Roitberg. 2019. Bounds on the Jensen gap, and implications for mean-concentrated distributions. AJMAA, Vol. 16, 14 (2019), 1--16.","journal-title":"AJMAA"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173--182.","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_2_15_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR."},{"key":"e_1_3_2_2_16_1","first-page":"21872","article-title":"Disentangled contrastive learning on graphs","volume":"34","author":"Li Haoyang","year":"2021","unstructured":"Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, and Wenwu Zhu. 2021. Disentangled contrastive learning on graphs. NIPS, Vol. 34 (2021), 21872--21884.","journal-title":"NIPS"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Zihan Lin Changxin Tian Yupeng Hou and Wayne Xin Zhao. 2022. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning. In WWW. 2320--2329.","DOI":"10.1145\/3485447.3512104"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Dongsheng Luo Wei Cheng Wenchao Yu Bo Zong Jingchao Ni Haifeng Chen and Xiang Zhang. 2021. Learning to drop: Robust graph neural network via topological denoising. In WSDM. 779--787.","DOI":"10.1145\/3437963.3441734"},{"key":"e_1_3_2_2_19_1","unstructured":"Jianxin Ma Peng Cui Kun Kuang Xin Wang and Wenwu Zhu. 2019a. Disentangled graph convolutional networks. In ICML. PMLR 4212--4221."},{"key":"e_1_3_2_2_20_1","unstructured":"Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019b. Learning disentangled representations for recommendation. In NIPS. 5711--5722."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3464304"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Zhen Peng Wenbing Huang Minnan Luo et al. 2020. Graph representation learning via graphical mutual information maximization. In WWW. 259--270.","DOI":"10.1145\/3366423.3380112"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742726"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Weiping Song Zhiping Xiao Yifan Wang Laurent Charlin Ming Zhang and Jian Tang. 2019. Session-based social recommendation via dynamic graph attention networks. In WSDM. 555--563.","DOI":"10.1145\/3289600.3290989"},{"key":"e_1_3_2_2_25_1","volume-title":"HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. In WWW. 593--601.","author":"Sun Jianing","year":"2021","unstructured":"Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe P\u00e9rez, and Maksims Volkovs. 2021. HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. In WWW. 593--601."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Changxin Tian Yuexiang Xie Yaliang Li Nan Yang and Wayne Xin Zhao. 2022. Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering. In SIGIR. 122--132.","DOI":"10.1145\/3477495.3531889"},{"key":"e_1_3_2_2_27_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_28_1","unstructured":"Petar Velickovic William Fedus William L Hamilton Pietro Li\u00f2 Yoshua Bengio and R Devon Hjelm. 2019. Deep Graph Infomax.. In ICLR."},{"key":"e_1_3_2_2_29_1","unstructured":"Tan Wang Jianqiang Huang Hanwang Zhang and Qianru Sun. 2020a. Visual commonsense r-cnn. In CVPR. 10760--10770."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Wenjie Wang Fuli Feng Xiangnan He Xiang Wang and Tat-Seng Chua. 2021a. Deconfounded recommendation for alleviating bias amplification. In KDD. 1717--1725.","DOI":"10.1145\/3447548.3467249"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019b. Neural Graph Collaborative Filtering. In SIGIR.","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Tinglin Huang Dingxian Wang Yancheng Yuan Zhenguang Liu Xiangnan He and Tat-Seng Chua. 2021b. Learning intents behind interactions with knowledge graph for recommendation. In WWW. 878--887.","DOI":"10.1145\/3442381.3450133"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Hongye Jin An Zhang Xiangnan He Tong Xu and Tat-Seng Chua. 2020b. Disentangled graph collaborative filtering. In SIGIR. 1001--1010.","DOI":"10.1145\/3397271.3401137"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6094"},{"key":"e_1_3_2_2_36_1","volume-title":"Disenhan: Disentangled heterogeneous graph attention network for recommendation. In CIKM. 1605--1614.","author":"Wang Yifan","year":"2020","unstructured":"Yifan Wang, Suyao Tang, et al. 2020c. Disenhan: Disentangled heterogeneous graph attention network for recommendation. In CIKM. 1605--1614."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Zhenyi Wang Huan Zhao and Chuan Shi. 2022. Profiling the Design Space for Graph Neural Networks based Collaborative Filtering. In WSDM. 1109--1119.","DOI":"10.1145\/3488560.3498520"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Wei Wei Chao Huang Lianghao Xia Yong Xu Jiashu Zhao and Dawei Yin. 2022. Contrastive meta learning with behavior multiplicity for recommendation. In WSDM. 1120--1128.","DOI":"10.1145\/3488560.3498527"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Jiancan Wu Xiang Wang Fuli Feng Xiangnan He Liang Chen Jianxun Lian et al. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726--735.","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_2_40_1","unstructured":"Shiwen Wu Fei Sun Wentao Zhang Xu Xie et al. 2020. Graph neural networks in recommender systems: a survey. ACM Computing Surveys (CSUR) (2020)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Lianghao Xia Chao Huang Chunzhen Huang Kangyi Lin Tao Yu and Ben Kao. 2023. Automated Self-Supervised Learning for Recommendation. In WWW. 992--1002.","DOI":"10.1145\/3543507.3583336"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Lianghao Xia Chao Huang Yong Xu Jiashu Zhao Dawei Yin and Jimmy Huang. 2022. Hypergraph contrastive collaborative filtering. In SIGIR. 70--79.","DOI":"10.1145\/3477495.3532058"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","unstructured":"Yuhao Yang Chao Huang Lianghao Xia Yuxuan Liang Yanwei Yu and Chenliang Li. 2022. Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In KDD. 2263--2274.","DOI":"10.1145\/3534678.3539342"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"crossref","unstructured":"Yonghui Yang Le Wu Richang Hong Kun Zhang and Meng Wang. 2021. Enhanced graph learning for collaborative filtering via mutual information maximization. In SIGIR. 71--80.","DOI":"10.1145\/3404835.3462928"},{"key":"e_1_3_2_2_45_1","volume-title":"Derek Zhiyuan Cheng, et al","author":"Yao Tiansheng","year":"2021","unstructured":"Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, et al. 2021. Self-supervised Learning for Large-scale Item Recommendations. In CIKM. 4321--4330."},{"key":"e_1_3_2_2_46_1","volume-title":"Nguyen Quoc Viet Hung, and Xiangliang Zhang","author":"Yu Junliang","year":"2021","unstructured":"Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang. 2021. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. In WWW. 413--424."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Shengyu Zhang Lingxiao Yang Dong Yao Yujie Lu Fuli Feng Zhou Zhao Tat-seng Chua and Fei Wu. 2022. Re4: Learning to Re-contrast Re-attend Re-construct for Multi-interest Recommendation. In WWW. 2216--2226.","DOI":"10.1145\/3485447.3512094"},{"key":"e_1_3_2_2_48_1","volume-title":"Multi-view intent disentangle graph networks for bundle recommendation. AAAI","author":"Zhao Sen","year":"2022","unstructured":"Sen Zhao, Wei Wei, Ding Zou, and Xianling Mao. 2022. Multi-view intent disentangle graph networks for bundle recommendation. AAAI (2022)."},{"key":"e_1_3_2_2_49_1","volume-title":"Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, et al.","author":"Zhou Kun","year":"2020","unstructured":"Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, et al. 2020. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In CIKM. 1893--1902."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Yaochen Zhu and Zhenzhong Chen. 2022. Mutually-regularized dual collaborative variational auto-encoder for recommendation systems. In WWW. 2379--2387.","DOI":"10.1145\/3485447.3512110"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Ding Zou Wei Wei Ziyang Wang Xian-Ling Mao Feida Zhu Rui Fang and Dangyang Chen. 2022. Improving knowledge-aware recommendation with multi-level interactive contrastive learning. In CIKM. 2817--2826.","DOI":"10.1145\/3511808.3557358"}],"event":{"name":"SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Taipei Taiwan","acronym":"SIGIR '23","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3591665","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3539618.3591665","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:40Z","timestamp":1750182700000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3591665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,18]]},"references-count":51,"alternative-id":["10.1145\/3539618.3591665","10.1145\/3539618"],"URL":"https:\/\/doi.org\/10.1145\/3539618.3591665","relation":{},"subject":[],"published":{"date-parts":[[2023,7,18]]},"assertion":[{"value":"2023-07-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}