{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:33:51Z","timestamp":1772120031171,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["Grant No. DP190101985, DP170103954"],"award-info":[{"award-number":["Grant No. DP190101985, DP170103954"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["No.2018YFB1004401"],"award-info":[{"award-number":["No.2018YFB1004401"]}]},{"name":"NSFC","award":["No.61532021, 61772537, 61772536, 61702522, 62076245"],"award-info":[{"award-number":["No.61532021, 61772537, 61772536, 61702522, 62076245"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,8]]},"DOI":"10.1145\/3437963.3441738","type":"proceedings-article","created":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T04:36:17Z","timestamp":1615005377000},"page":"265-273","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":79,"title":["Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation"],"prefix":"10.1145","author":[{"given":"Bowen","family":"Hao","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}]},{"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}]},{"given":"Cuiping","family":"Li","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}]},{"given":"Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Spectral Networks and Locally Connected Networks on Graphs. In ICLR'14","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann LeCun . 2014 . Spectral Networks and Locally Connected Networks on Graphs. In ICLR'14 . Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. In ICLR'14."},{"key":"e_1_3_2_2_2_1","volume-title":"Exploiting Centrality Information with Graph Convolutions for Network Representation Learning. In ICDE'19","author":"Chen Hongxu","year":"2019","unstructured":"Hongxu Chen , Hongzhi Yin , Tong Chen , Quoc Viet Hung Nguyen , Wen-Chih Peng , and Xue Li . 2019 . Exploiting Centrality Information with Graph Convolutions for Network Representation Learning. In ICDE'19 . 590--601. Hongxu Chen, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wen-Chih Peng, and Xue Li. 2019. Exploiting Centrality Information with Graph Convolutions for Network Representation Learning. In ICDE'19. 590--601."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2982878"},{"key":"e_1_3_2_2_4_1","unstructured":"Jie Chen Tengfei Ma and Cao Xiao. 2018a. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR' 18.  Jie Chen Tengfei Ma and Cao Xiao. 2018a. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR' 18."},{"key":"e_1_3_2_2_5_1","volume-title":"Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML'18","volume":"80","author":"Chen Jianfei","year":"2018","unstructured":"Jianfei Chen , Jun Zhu , and Le Song . 2018 b. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML'18 , Vol. 80 . 941--949. Jianfei Chen, Jun Zhu, and Le Song. 2018b. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML'18, Vol. 80. 941--949."},{"key":"e_1_3_2_2_6_1","unstructured":"Micha\u00eb l Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In NeurlPS'16. 3837--3845.  Micha\u00eb l Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In NeurlPS'16. 3837--3845."},{"key":"e_1_3_2_2_7_1","volume-title":"Sequential Scenario-Specific Meta Learner for Online Recommendation. In SIGKDD'19","author":"Du Zhengxiao","year":"2019","unstructured":"Zhengxiao Du , Xiaowei Wang , Hongxia Yang , Jingren Zhou , and Jie Tang . 2019 . Sequential Scenario-Specific Meta Learner for Online Recommendation. In SIGKDD'19 . 2895--2904. Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, and Jie Tang. 2019. Sequential Scenario-Specific Meta Learner for Online Recommendation. In SIGKDD'19. 2895--2904."},{"key":"e_1_3_2_2_8_1","volume-title":"Reinforcement Learning for Relation Classification From Noisy Data. In AAAI'18","author":"Feng Jun","year":"2018","unstructured":"Jun Feng , Minlie Huang , Li Zhao , Yang Yang , and Xiaoyan Zhu . 2018 . Reinforcement Learning for Relation Classification From Noisy Data. In AAAI'18 . 5779--5786. Jun Feng, Minlie Huang, Li Zhao, Yang Yang, and Xiaoyan Zhu. 2018. Reinforcement Learning for Relation Classification From Noisy Data. In AAAI'18. 5779--5786."},{"key":"e_1_3_2_2_9_1","volume-title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In ICML'17","volume":"70","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn , Pieter Abbeel , and Sergey Levine . 2017 . Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In ICML'17 , Vol. 70 . 1126--1135. Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In ICML'17, Vol. 70. 1126--1135."},{"key":"e_1_3_2_2_10_1","first-page":"12","article-title":"Deep Learning for User Interest and Response Prediction in Online Display Advertising. Data Sci","volume":"5","author":"Gharibshah Zhabiz","year":"2020","unstructured":"Zhabiz Gharibshah , Xingquan Zhu , Arthur Hainline , and Michael Conway . 2020 . Deep Learning for User Interest and Response Prediction in Online Display Advertising. Data Sci . Eng. , Vol. 5 , 1 (2020), 12 -- 26 . Zhabiz Gharibshah, Xingquan Zhu, Arthur Hainline, and Michael Conway. 2020. Deep Learning for User Interest and Response Prediction in Online Display Advertising. Data Sci. Eng., Vol. 5, 1 (2020), 12--26.","journal-title":"Eng."},{"key":"e_1_3_2_2_11_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurlPS'17. 1024--1034.  William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurlPS'17. 1024--1034."},{"key":"e_1_3_2_2_12_1","volume-title":"Konstan","author":"Maxwell Harper F.","year":"2016","unstructured":"F. Maxwell Harper and Joseph A . Konstan . 2016 . The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst . (2016), 19:1--19:19. F. Maxwell Harper and Joseph A. Konstan. 2016. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. (2016), 19:1--19:19."},{"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","volume-title":"Neural Collaborative Filtering. In WWW'17","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He , Lizi Liao , Hanwang Zhang , Liqiang Nie , Xia Hu , and Tat-Seng Chua . 2017 . Neural Collaborative Filtering. In WWW'17 . 173--182. Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In WWW'17. 173--182."},{"key":"e_1_3_2_2_15_1","volume-title":"Strategies for Pre-training Graph Neural Networks. In ICLR'20","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu , Bowen Liu , Joseph Gomes , Marinka Zitnik , Percy Liang , Vijay S. Pande , and Jure Leskovec . 2020 b . Strategies for Pre-training Graph Neural Networks. In ICLR'20 . Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec. 2020 b. Strategies for Pre-training Graph Neural Networks. In ICLR'20."},{"key":"e_1_3_2_2_16_1","volume-title":"Few-Shot Representation Learning for Out-Of-Vocabulary Words. In ACL'19","author":"Hu Ziniu","year":"2019","unstructured":"Ziniu Hu , Ting Chen , Kai-Wei Chang , and Yizhou Sun . 2019 . Few-Shot Representation Learning for Out-Of-Vocabulary Words. In ACL'19 . 4102--4112. Ziniu Hu, Ting Chen, Kai-Wei Chang, and Yizhou Sun. 2019. Few-Shot Representation Learning for Out-Of-Vocabulary Words. In ACL'19. 4102--4112."},{"key":"e_1_3_2_2_17_1","volume-title":"GPT-GNN: Generative Pre-Training of Graph Neural Networks. In SIGKDD'20","author":"Hu Ziniu","year":"2020","unstructured":"Ziniu Hu , Yuxiao Dong , Kuansan Wang , Kai-Wei Chang , and Yizhou Sun . 2020 a . GPT-GNN: Generative Pre-Training of Graph Neural Networks. In SIGKDD'20 . Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun. 2020 a. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In SIGKDD'20."},{"key":"e_1_3_2_2_18_1","unstructured":"Wenbing Huang Tong Zhang Yu Rong and Junzhou Huang. 2018. Adaptive Sampling Towards Fast Graph Representation Learning. In NeurlPS' 18. 4563--4572.  Wenbing Huang Tong Zhang Yu Rong and Junzhou Huang. 2018. Adaptive Sampling Towards Fast Graph Representation Learning. In NeurlPS' 18. 4563--4572."},{"key":"e_1_3_2_2_19_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In ICLR'17","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR'17 . Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR'17."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330859"},{"key":"e_1_3_2_2_21_1","volume-title":"Item-to-Item Collaborative Filtering","author":"Linden Greg","year":"2003","unstructured":"Greg Linden , Brent Smith , and Jeremy York . 2003. Amazon.com Recommendations : Item-to-Item Collaborative Filtering . IEEE Internet Comput . ( 2003 ), 76--80. Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Comput. (2003), 76--80."},{"key":"e_1_3_2_2_22_1","unstructured":"Yuanfu Lu Yuan Fang and Chuan Shi. 2020. Meta-learning on heterogeneous information networks for cold-start recommendation. (2020).  Yuanfu Lu Yuan Fang and Chuan Shi. 2020. Meta-learning on heterogeneous information networks for cold-start recommendation. (2020)."},{"key":"e_1_3_2_2_23_1","volume-title":"Meta Networks. In ICML'17 (Proceedings of Machine Learning Research","volume":"2563","author":"Munkhdalai Tsendsuren","year":"2017","unstructured":"Tsendsuren Munkhdalai and Hong Yu . 2017 . Meta Networks. In ICML'17 (Proceedings of Machine Learning Research , Vol. 70),, Doina Precup and Yee Whye Teh (Eds.). 2554-- 2563 . Tsendsuren Munkhdalai and Hong Yu. 2017. Meta Networks. In ICML'17 (Proceedings of Machine Learning Research, Vol. 70),, Doina Precup and Yee Whye Teh (Eds.). 2554--2563."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331268"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_2_26_1","volume-title":"Zemel","author":"Snell Jake","year":"2017","unstructured":"Jake Snell , Kevin Swersky , and Richard S . Zemel . 2017 . Prototypical Networks for Few-shot Learning. In NeurlPS '17. 4077--4087. Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017. Prototypical Networks for Few-shot Learning. In NeurlPS'17. 4077--4087."},{"key":"e_1_3_2_2_27_1","volume-title":"ICLR'20","author":"Sun Fan-Yun","year":"2020","unstructured":"Fan-Yun Sun , Jordan Hoffmann , Vikas Verma , and Jian Tang . 2020 . InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization . In ICLR'20 . Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2020. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In ICLR'20."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017072"},{"key":"e_1_3_2_2_29_1","unstructured":"Manasi Vartak Arvind Thiagarajan Conrado Miranda Jeshua Bratman and Hugo Larochelle. 2017. A Meta-Learning Perspective on Cold-Start Recommendations for Items. In NeurlPS'17. 6904--6914.  Manasi Vartak Arvind Thiagarajan Conrado Miranda Jeshua Bratman and Hugo Larochelle. 2017. A Meta-Learning Perspective on Cold-Start Recommendations for Items. In NeurlPS'17. 6904--6914."},{"key":"e_1_3_2_2_30_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NeurlPS'17. 5998--6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NeurlPS'17. 5998--6008."},{"key":"e_1_3_2_2_31_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR' 18.  Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR' 18."},{"key":"e_1_3_2_2_32_1","volume-title":"Deep Graph Infomax. In ICLR'19","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. In ICLR'19 . Petar Velickovic, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. In ICLR'19."},{"key":"e_1_3_2_2_33_1","volume-title":"A perspective view and survey of meta-learning. Artificial intelligence review","author":"Vilalta Ricardo","year":"2002","unstructured":"Ricardo Vilalta and Youssef Drissi . 2002. A perspective view and survey of meta-learning. Artificial intelligence review , Vol. 18 , 2 ( 2002 ), 77--95. Ricardo Vilalta and Youssef Drissi. 2002. A perspective view and survey of meta-learning. Artificial intelligence review, Vol. 18, 2 (2002), 77--95."},{"key":"e_1_3_2_2_34_1","unstructured":"Oriol Vinyals Charles Blundell Tim Lillicrap Koray Kavukcuoglu and Daan Wierstra. 2016. Matching Networks for One Shot Learning. In NeurlPS'16. 3630--3638.  Oriol Vinyals Charles Blundell Tim Lillicrap Koray Kavukcuoglu and Daan Wierstra. 2016. Matching Networks for One Shot Learning. In NeurlPS'16. 3630--3638."},{"key":"e_1_3_2_2_35_1","first-page":"2000","article-title":"d","volume":"19","author":"Wang Hongwei","year":"2019","unstructured":"Hongwei Wang , Fuzheng Zhang , Miao Zhao , Wenjie Li , Xing Xie , and Minyi Guo . 2019 d . Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. In WWW' 19. 2000 -- 2010 . Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019 d. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. In WWW' 19. 2000--2010.","journal-title":"Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. In WWW'"},{"key":"e_1_3_2_2_36_1","volume-title":"Enhancing Collaborative Filtering with Generative Augmentation. In SIGKDD'19","author":"Wang Qinyong","year":"2019","unstructured":"Qinyong Wang , Hongzhi Yin , Hao Wang , Quoc Viet Hung Nguyen , Zi Huang , and Lizhen Cui . 2019 c . Enhancing Collaborative Filtering with Generative Augmentation. In SIGKDD'19 . ACM, 548--556. Qinyong Wang, Hongzhi Yin, Hao Wang, Quoc Viet Hung Nguyen, Zi Huang, and Lizhen Cui. 2019 c. Enhancing Collaborative Filtering with Generative Augmentation. In SIGKDD'19. ACM, 548--556."},{"key":"e_1_3_2_2_37_1","volume-title":"KGAT: Knowledge Graph Attention Network for Recommendation. In SIGKDD'19","author":"Wang Xiang","year":"2019","unstructured":"Xiang Wang , Xiangnan He , Yixin Cao , Meng Liu , and Tat-Seng Chua . 2019 a . KGAT: Knowledge Graph Attention Network for Recommendation. In SIGKDD'19 . 950--958. Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019 a. KGAT: Knowledge Graph Attention Network for Recommendation. In SIGKDD'19. 950--958."},{"key":"e_1_3_2_2_38_1","volume-title":"Neural Graph Collaborative Filtering. In SIGIR'19","author":"Wang Xiang","year":"2019","unstructured":"Xiang Wang , Xiangnan He , Meng Wang , Fuli Feng , and Tat-Seng Chua . 2019 b . Neural Graph Collaborative Filtering. In SIGIR'19 . 165--174. Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 b. Neural Graph Collaborative Filtering. In SIGIR'19. 165--174."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629461"},{"key":"e_1_3_2_2_41_1","volume-title":"Social Influence-Based Group Representation Learning for Group Recommendation. In ICDE'19","author":"Yin Hongzhi","year":"2019","unstructured":"Hongzhi Yin , Qinyong Wang , Kai Zheng , Zhixu Li , Jiali Yang , and Xiaofang Zhou . 2019 . Social Influence-Based Group Representation Learning for Group Recommendation. In ICDE'19 . IEEE, 566--577. Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Jiali Yang, and Xiaofang Zhou. 2019. Social Influence-Based Group Representation Learning for Group Recommendation. In ICDE'19. IEEE, 566--577."},{"key":"e_1_3_2_2_42_1","volume-title":"Overcoming Data Sparsity in Group Recommendation. TKDE\"20","author":"Yin Hongzhi","year":"2020","unstructured":"Hongzhi Yin , Qinyong Wang , Kai Zheng , Zhixu Li , and Xiaofang Zhou . 2020. Overcoming Data Sparsity in Group Recommendation. TKDE\"20 , Vol. abs\/ 2010 .00813 ( 2020 ). Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, and Xiaofang Zhou. 2020. Overcoming Data Sparsity in Group Recommendation. TKDE\"20, Vol. abs\/2010.00813 (2020)."},{"key":"e_1_3_2_2_43_1","volume-title":"Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation. TKDE'19 11","author":"Yin Hongzhi","year":"2017","unstructured":"Hongzhi Yin , Weiqing Wang , Hao Wang , Ling Chen , and Xiaofang Zhou . 2017 a. Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation. TKDE'19 11 (2017), 2537--2551. Hongzhi Yin, Weiqing Wang, Hao Wang, Ling Chen, and Xiaofang Zhou. 2017a. Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation. TKDE'19 11 (2017), 2537--2551."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2741484"},{"key":"e_1_3_2_2_45_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 SIGKDD\"18. 974--983.  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 SIGKDD\"18. 974--983.","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_46_1","volume-title":"ICML'20","author":"You Yuning","year":"2020","unstructured":"Yuning You , Tianlong Chen , Zhangyang Wang , and Yang Shen . 2020 . When Does Self-Supervision Help Graph Convolutional Networks? . In ICML'20 . Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen. 2020. When Does Self-Supervision Help Graph Convolutional Networks?. In ICML'20."},{"key":"e_1_3_2_2_47_1","volume-title":"Hierarchical Reinforcement Learning for Course Recommendation in MOOCs. In AAAI'19","author":"Zhang Jing","year":"2019","unstructured":"Jing Zhang , Bowen Hao , Bo Chen , Cuiping Li , Hong Chen , and Jimeng Sun . 2019 . Hierarchical Reinforcement Learning for Course Recommendation in MOOCs. In AAAI'19 . 435--442. Jing Zhang, Bowen Hao, Bo Chen, Cuiping Li, Hong Chen, and Jimeng Sun. 2019. Hierarchical Reinforcement Learning for Course Recommendation in MOOCs. In AAAI'19. 435--442."}],"event":{"name":"WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining","location":"Virtual Event Israel","acronym":"WSDM '21","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 14th ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441738","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3437963.3441738","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:34Z","timestamp":1750193254000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441738"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":47,"alternative-id":["10.1145\/3437963.3441738","10.1145\/3437963"],"URL":"https:\/\/doi.org\/10.1145\/3437963.3441738","relation":{},"subject":[],"published":{"date-parts":[[2021,3,8]]},"assertion":[{"value":"2021-03-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}