{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:10:08Z","timestamp":1774487408591,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":58,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"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":[[2022,2,11]]},"DOI":"10.1145\/3488560.3498527","type":"proceedings-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T21:42:57Z","timestamp":1644961377000},"page":"1120-1128","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":181,"title":["Contrastive Meta Learning with Behavior Multiplicity for Recommendation"],"prefix":"10.1145","author":[{"given":"Wei","family":"Wei","sequence":"first","affiliation":[{"name":"University of Hong Kong &amp; South China University of Technology, Hong Kong, Hong Kong"}]},{"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong, Hong Kong"}]},{"given":"Lianghao","family":"Xia","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong, China"}]},{"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China"}]},{"given":"Jiashu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Wilfrid Laurier University, Waterloo, Canada"}]},{"given":"Dawei","family":"Yin","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16515"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5329"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Jingyuan Chen Hanwang Zhang Xiangnan He Liqiang Nie Wei Liu and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In SIGIR. 335--344.","DOI":"10.1145\/3077136.3080797"},{"key":"e_1_3_2_2_5_1","unstructured":"Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020 a. A simple framework for contrastive learning of visual representations. In ICML. PMLR 1597--1607."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Yu Deng Jiaolong Yang Dong Chen Fang Wen and Xin Tong. 2020. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In CVPR . 5154--5163.","DOI":"10.1109\/CVPR42600.2020.00520"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Shaohua Fan Junxiong Zhu Xiaotian Han Chuan Shi Linmei Hu Biyu Ma and Yongliang Li. 2019 b. Metapath-guided heterogeneous graph neural network for intent recommendation. In KDD . 2478--2486.","DOI":"10.1145\/3292500.3330673"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019 a. Graph neural networks for social recommendation. In WWW. 417--426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_2_9_1","unstructured":"Luca Franceschi Paolo Frasconi Saverio Salzo Riccardo Grazzi and Massimiliano Pontil. 2018. Bilevel programming for hyperparameter optimization and meta-learning. In ICML. PMLR 1568--1577."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17518"},{"key":"e_1_3_2_2_11_1","volume-title":"2019 a. Neural multi-task recommendation from multi-behavior data","author":"Gao Chen","unstructured":"Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, and Depeng Jin. 2019 a. Neural multi-task recommendation from multi-behavior data. In ICDE. IEEE, 1554--1557."},{"key":"e_1_3_2_2_12_1","volume-title":"2019 b. Learning to recommend with multiple cascading behaviors. TKDE","author":"Gao Chen","year":"2019","unstructured":"Chen Gao, Xiangnan He, Danhua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Lina Yao, Yang Song, and Depeng Jin. 2019 b. Learning to recommend with multiple cascading behaviors. TKDE (2019)."},{"key":"e_1_3_2_2_13_1","unstructured":"Spyros Gidaris Praveer Singh and Nikos Komodakis. 2018. Unsupervised representation learning by predicting image rotations. In ICLR ."},{"key":"e_1_3_2_2_14_1","volume-title":"AISTATS. JMLR Workshop and Conference Proceedings, 249--256","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS. JMLR Workshop and Conference Proceedings, 249--256."},{"key":"e_1_3_2_2_15_1","unstructured":"Xiangnan He Kuan Deng Xiang Wang Yan Li Yongdong Zhang and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR. ACM."},{"key":"e_1_3_2_2_16_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_17_1","doi-asserted-by":"crossref","unstructured":"Ziniu Hu Yuxiao Dong Kuansan Wang and Yizhou Sun. 2020. Heterogeneous graph transformer. In WWW. 2704--2710.","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Chao Huang. 2021. Recent Advances in Heterogeneous Relation Learning for Recommendation. In IJCAI .","DOI":"10.24963\/ijcai.2021\/606"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16534"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Chao Huang Xian Wu Xuchao Zhang Chuxu Zhang Jiashu Zhao Dawei Yin and Nitesh V Chawla. 2019. Online purchase prediction via multi-scale modeling of behavior dynamics. In KDD . 2613--2622.","DOI":"10.1145\/3292500.3330790"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Chao Huang Huance Xu Yong Xu Peng Dai Lianghao Xia Mengyin Lu Liefeng Bo Hao Xing Xiaoping Lai and Yanfang Ye. 2021 b. Knowledge-aware coupled graph neural network for social recommendation. In AAAI .","DOI":"10.1609\/aaai.v35i5.16533"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16097"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Bowen Jin Chen Gao Xiangnan He Depeng Jin et al. 2020. Multi-behavior recommendation with graph convolutional networks. In SIGIR. 659--668.","DOI":"10.1145\/3397271.3401072"},{"key":"e_1_3_2_2_24_1","volume-title":"Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph. TKDE","author":"Liu Yong","year":"2021","unstructured":"Yong Liu, Susen Yang, Yonghui Xu, Chunyan Miao, Min Wu, and Juyong Zhang. 2021. Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph. TKDE (2021)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Xiaoling Long Chao Huang Yong Xu Huance Xu Peng Dai Lianghao Xia and Liefeng Bo. 2021. Social Recommendation with Self-Supervised Metagraph Informax Network. In CIKM . 1160--1169.","DOI":"10.1145\/3459637.3482480"},{"key":"e_1_3_2_2_26_1","unstructured":"Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. In ICLR ."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Yuanfu Lu Yuan Fang and Chuan Shi. 2020. Meta-learning on heterogeneous information networks for cold-start recommendation. In KDD . 1563--1573.","DOI":"10.1145\/3394486.3403207"},{"key":"e_1_3_2_2_28_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_29_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Tao Qi Fangzhao Wu Chuhan Wu and Yongfeng Huang. 2021. Personalized News Recommendation with Knowledge-aware Interactive Matching. In SIGIR .","DOI":"10.1145\/3404835.3462861"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_2_32_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback. UAI","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. UAI (2009)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742726"},{"key":"e_1_3_2_2_34_1","volume-title":"Meta-weight-net: Learning an explicit mapping for sample weighting.","author":"Shu Jun","year":"2019","unstructured":"Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng. 2019. Meta-weight-net: Learning an explicit mapping for sample weighting. (2019)."},{"key":"e_1_3_2_2_35_1","volume-title":"Cyclical learning rates for training neural networks","author":"Smith Leslie N","unstructured":"Leslie N Smith. 2017. Cyclical learning rates for training neural networks. In WACV. IEEE, 464--472."},{"key":"e_1_3_2_2_36_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_37_1","doi-asserted-by":"crossref","unstructured":"Md Mehrab Tanjim Congzhe Su Ethan Benjamin Diane Hu Liangjie Hong and Julian McAuley. 2020. Attentive sequential models of latent intent for next item recommendation. In WWW . 2528--2534.","DOI":"10.1145\/3366423.3380002"},{"key":"e_1_3_2_2_38_1","volume-title":"JMLR","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. JMLR , Vol. 9, 11 (2008)."},{"key":"e_1_3_2_2_39_1","first-page":"4","article-title":"Deep Graph Infomax","volume":"2","author":"Velickovic Petar","year":"2019","unstructured":"Petar Velickovic, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. ICLR , Vol. 2, 3 (2019), 4.","journal-title":"ICLR"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Jizhe Wang Pipei Huang Huan Zhao Zhibo Zhang Binqiang Zhao and Dik Lun Lee. 2018. Billion-scale commodity embedding for e-commerce recommendation in alibaba. In KDD . 839--848.","DOI":"10.1145\/3219819.3219869"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Wen Wang Wei Zhang Shukai Liu Qi Liu Bo Zhang Leyu Lin and Hongyuan Zha. 2020 b. Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In WWW. 3056--3062.","DOI":"10.1145\/3366423.3380077"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Xiangnan He Yixin Cao Meng Liu and Tat-Seng Chua. 2019 a. Kgat: Knowledge graph attention network for recommendation. In KDD . 950--958.","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019 b. Neural graph collaborative filtering. In SIGIR. ACM 165--174.","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"crossref","unstructured":"Xiao Wang Nian Liu Hui Han and Chuan Shi. 2021. Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning. In KDD .","DOI":"10.1145\/3447548.3467415"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6094"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"crossref","unstructured":"Yinwei Wei Xiang Wang Liqiang Nie Xiangnan He and Tat-Seng Chua. 2020. Graph-refined convolutional network for multimedia recommendation with implicit feedback. In MM . 3541--3549.","DOI":"10.1145\/3394171.3413556"},{"key":"e_1_3_2_2_47_1","volume-title":"Reviews meet graphs: Enhancing user and item representations for recommendation with hierarchical attentive graph neural network. In EMNLP. 4886--4895","author":"Wu Chuhan","year":"2019","unstructured":"Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, and Xing Xie. 2019. Reviews meet graphs: Enhancing user and item representations for recommendation with hierarchical attentive graph neural network. In EMNLP. 4886--4895."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Jiancan Wu Xiang Wang Fuli Feng Xiangnan He Liang Chen Jianxun Lian and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726--735.","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Yao Wu Christopher DuBois Alice X Zheng and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In WSDM . 153--162.","DOI":"10.1145\/2835776.2835837"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Lianghao Xia Chao Huang Yong Xu Peng Dai Bo Zhang and Liefeng Bo. 2020. Multiplex behavioral relation learning for recommendation via memory augmented transformer network. In SIGIR. 2397--2406.","DOI":"10.1145\/3397271.3401445"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16576"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Lianghao Xia Yong Xu Chao Huang Peng Dai and Liefeng Bo. 2021 b. Graph meta network for multi-behavior recommendation. In SIGIR . 757--766.","DOI":"10.1145\/3404835.3462972"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"crossref","unstructured":"Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William L Hamilton and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In KDD. ACM 974--983.","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_54_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. NIPS , Vol. 33 (2020), 5812--5823.","journal-title":"NIPS"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"crossref","unstructured":"Chuxu Zhang Dongjin Song Chao Huang Ananthram Swami and Nitesh V Chawla. 2019. Heterogeneous graph neural network. In KDD. 793--803.","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"crossref","unstructured":"Hanwang Zhang Yang Yang Huanbo Luan Shuicheng Yang and Tat-Seng Chua. 2014. Start from scratch: Towards automatically identifying modeling and naming visual attributes. In MM. 187--196.","DOI":"10.1145\/2647868.2654915"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"crossref","unstructured":"Lei Zheng Chun-Ta Lu Fei Jiang Jiawei Zhang and Philip S Yu. 2018. Spectral collaborative filtering. In Recsys. 311--319.","DOI":"10.1145\/3240323.3240343"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"crossref","unstructured":"Yanqiao Zhu Yichen Xu Feng Yu Qiang Liu Shu Wu and Liang Wang. 2021. Graph contrastive learning with adaptive augmentation. In WWW . 2069--2080.","DOI":"10.1145\/3442381.3449802"}],"event":{"name":"WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining","location":"Virtual Event AZ USA","acronym":"WSDM '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498527","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488560.3498527","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:20Z","timestamp":1750188680000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":58,"alternative-id":["10.1145\/3488560.3498527","10.1145\/3488560"],"URL":"https:\/\/doi.org\/10.1145\/3488560.3498527","relation":{},"subject":[],"published":{"date-parts":[[2022,2,11]]},"assertion":[{"value":"2022-02-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}