{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:12:53Z","timestamp":1750219973014,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Beijing Social Science Foundation Project Key Project of Social Science Program of Beijing Education Commission","award":["SZ202011232024"],"award-info":[{"award-number":["SZ202011232024"]}]},{"name":"Education Humanities and Social Sciences Planning Fund Project","award":["20YJAZH129"],"award-info":[{"award-number":["20YJAZH129"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,21]]},"DOI":"10.1145\/3569966.3570002","type":"proceedings-article","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T22:24:41Z","timestamp":1671575081000},"page":"121-128","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Sampling Light Graph Collaborative Filtering for Social Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9044-2440","authenticated-orcid":false,"given":"Yefan","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Information and Management, Beijing Information Science & Technology University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4702-503X","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Management, Beijing Information Science & Technology University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0332-5690","authenticated-orcid":false,"given":"Siqi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Management, Beijing Information Science & Technology University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,12,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3501396"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"J.-T. Huang \"Embedding-based retrieval in facebook search \" in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 pp. 2553-2561.  J.-T. Huang \"Embedding-based retrieval in facebook search \" in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 pp. 2553-2561.","DOI":"10.1145\/3394486.3403305"},{"key":"e_1_3_2_1_3_1","volume-title":"A survey of collaborative filtering techniques,\" Advances in artificial intelligence","author":"Su X.","year":"2009","unstructured":"X. Su and T. M. Khoshgoftaar , \" A survey of collaborative filtering techniques,\" Advances in artificial intelligence , vol. 2009 , 2009 . X. Su and T. M. Khoshgoftaar, \"A survey of collaborative filtering techniques,\" Advances in artificial intelligence, vol. 2009, 2009."},{"key":"e_1_3_2_1_4_1","first-page":"165","article-title":"Neural graph collaborative filtering,\" in Proceedings of the 42nd international ACM SIGIR conference on Research and development","author":"Wang X.","year":"2019","unstructured":"X. Wang , X. He , M. Wang , F. Feng , and T.-S. Chua , \" Neural graph collaborative filtering,\" in Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval , 2019 , pp. 165 - 174 . X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, \"Neural graph collaborative filtering,\" in Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, 2019, pp. 165-174.","journal-title":"Information Retrieval"},{"key":"e_1_3_2_1_5_1","first-page":"639","article-title":"Lightgcn: Simplifying and powering graph convolution network for recommendation,\" in Proceedings of the 43rd International ACM SIGIR conference on research and development","author":"He X.","year":"2020","unstructured":"X. He , K. Deng , X. Wang , Y. Li , Y. Zhang , and M. Wang , \" Lightgcn: Simplifying and powering graph convolution network for recommendation,\" in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , 2020 , pp. 639 - 648 . X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, \"Lightgcn: Simplifying and powering graph convolution network for recommendation,\" in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020, pp. 639-648.","journal-title":"Information Retrieval"},{"key":"e_1_3_2_1_6_1","volume-title":"Light Graph Convolution Network for Social Recommendation,\" Information Sciences","author":"Liao J.","year":"2022","unstructured":"J. Liao , \"SocialLGN : Light Graph Convolution Network for Social Recommendation,\" Information Sciences , 2022 . J. Liao , \"SocialLGN: Light Graph Convolution Network for Social Recommendation,\" Information Sciences, 2022."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-013-0141-9"},{"key":"e_1_3_2_1_8_1","first-page":"3","volume-title":"Springer","author":"Lu Y.","unstructured":"Y. Lu , \"Social influence attentive neural network for friend-enhanced recommendation,\" in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2020 : Springer , pp. 3 - 18 . Y. Lu , \"Social influence attentive neural network for friend-enhanced recommendation,\" in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2020: Springer, pp. 3-18."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2641564"},{"key":"e_1_3_2_1_10_1","first-page":"931","volume-title":"Sorec: social recommendation using probabilistic matrix factorization,\" in Proceedings of the 17th ACM conference on Information and knowledge management","author":"Ma H.","year":"2008","unstructured":"H. Ma , H. Yang , M. R. Lyu , and I. King , \" Sorec: social recommendation using probabilistic matrix factorization,\" in Proceedings of the 17th ACM conference on Information and knowledge management , 2008 , pp. 931 - 940 . H. Ma, H. Yang, M. R. Lyu, and I. King, \"Sorec: social recommendation using probabilistic matrix factorization,\" in Proceedings of the 17th ACM conference on Information and knowledge management, 2008, pp. 931-940."},{"key":"e_1_3_2_1_11_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback,\" arXiv preprint arXiv:1205.2618","author":"Rendle S.","year":"2012","unstructured":"S. Rendle , C. Freudenthaler , Z. Gantner , and L. Schmidt-Thieme , \" BPR: Bayesian personalized ranking from implicit feedback,\" arXiv preprint arXiv:1205.2618 , 2012 . S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, \"BPR: Bayesian personalized ranking from implicit feedback,\" arXiv preprint arXiv:1205.2618, 2012."},{"key":"e_1_3_2_1_12_1","volume-title":"Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings,\" in Proceedings of the AAAI conference on artificial intelligence","author":"Guo G.","year":"2015","unstructured":"G. Guo , J. Zhang , and N. Yorke-Smith , \" Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings,\" in Proceedings of the AAAI conference on artificial intelligence , 2015 , vol. 29 , no. 1. G. Guo, J. Zhang, and N. Yorke-Smith, \"Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings,\" in Proceedings of the AAAI conference on artificial intelligence, 2015, vol. 29, no. 1."},{"key":"e_1_3_2_1_13_1","volume-title":"ContextMF: A Fast and Context-aware Embedding Learning Method for Recommendation Systems","author":"Wang J.","year":"2018","unstructured":"J. Wang , D. Bagul , and S. Srihari , \" ContextMF: A Fast and Context-aware Embedding Learning Method for Recommendation Systems ,\" 2018 . J. Wang, D. Bagul, and S. Srihari, \"ContextMF: A Fast and Context-aware Embedding Learning Method for Recommendation Systems,\" 2018."},{"volume-title":"Social collaborative filtering by trust,\" IEEE transactions on pattern analysis and machine intelligence","author":"Yang B.","key":"e_1_3_2_1_14_1","unstructured":"B. Yang , Y. Lei , J. Liu , and W. Li , \" Social collaborative filtering by trust,\" IEEE transactions on pattern analysis and machine intelligence , vol. 39 , no. 8, pp. 1633-1647, 2016. B. Yang, Y. Lei, J. Liu, and W. Li, \"Social collaborative filtering by trust,\" IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 8, pp. 1633-1647, 2016."},{"key":"e_1_3_2_1_15_1","volume-title":"User Embedding based Neighborhood Aggregation Method for Inductive Recommendation,\" arXiv preprint arXiv:2102.07575","author":"Ragesh R.","year":"2021","unstructured":"R. Ragesh , S. Sellamanickam , V. Lingam , A. Iyer , and R. Bairi , \" User Embedding based Neighborhood Aggregation Method for Inductive Recommendation,\" arXiv preprint arXiv:2102.07575 , 2021 . R. Ragesh, S. Sellamanickam, V. Lingam, A. Iyer, and R. Bairi, \"User Embedding based Neighborhood Aggregation Method for Inductive Recommendation,\" arXiv preprint arXiv:2102.07575, 2021."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"W. Fan \"Graph neural networks for social recommendation \" in The world wide web conference 2019 pp. 417-426.  W. Fan \"Graph neural networks for social recommendation \" in The world wide web conference 2019 pp. 417-426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_17_1","volume-title":"The strange geometry of skip-gram with negative sampling,\" in Empirical Methods in Natural Language Processing","author":"Mimno D.","year":"2017","unstructured":"D. Mimno and L. Thompson , \" The strange geometry of skip-gram with negative sampling,\" in Empirical Methods in Natural Language Processing , 2017 . D. Mimno and L. Thompson, \"The strange geometry of skip-gram with negative sampling,\" in Empirical Methods in Natural Language Processing, 2017."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313416"},{"key":"e_1_3_2_1_19_1","first-page":"1666","volume-title":"Understanding negative sampling in graph representation learning,\" in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Yang Z.","year":"2020","unstructured":"Z. Yang , M. Ding , C. Zhou , H. Yang , J. Zhou , and J. Tang , \" Understanding negative sampling in graph representation learning,\" in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020 , pp. 1666 - 1676 . Z. Yang, M. Ding, C. Zhou, H. Yang, J. Zhou, and J. Tang, \"Understanding negative sampling in graph representation learning,\" in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 1666-1676."},{"key":"e_1_3_2_1_20_1","first-page":"785","volume-title":"Optimizing top-n collaborative filtering via dynamic negative item sampling,\" in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","author":"Zhang W.","year":"2013","unstructured":"W. Zhang , T. Chen , J. Wang , and Y. Yu , \" Optimizing top-n collaborative filtering via dynamic negative item sampling,\" in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval , 2013 , pp. 785 - 788 . W. Zhang, T. Chen, J. Wang, and Y. Yu, \"Optimizing top-n collaborative filtering via dynamic negative item sampling,\" in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, 2013, pp. 785-788."},{"key":"e_1_3_2_1_21_1","first-page":"974","volume-title":"Graph convolutional neural networks for web-scale recommender systems,\" in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining","author":"Ying R.","year":"2018","unstructured":"R. Ying , R. He , K. Chen , P. Eksombatchai , W. L. Hamilton , and J. Leskovec , \" Graph convolutional neural networks for web-scale recommender systems,\" in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining , 2018 , pp. 974 - 983 . R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, \"Graph convolutional neural networks for web-scale recommender systems,\" in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 2018, pp. 974-983."},{"key":"e_1_3_2_1_22_1","first-page":"665","volume-title":"An Improved Training Method for Graph Neural Network-based Recommender Systems,\" in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Huang T.","year":"2021","unstructured":"T. Huang , \"MixGCF : An Improved Training Method for Graph Neural Network-based Recommender Systems,\" in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining , 2021 , pp. 665 - 674 . T. Huang , \"MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems,\" in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 665-674."},{"key":"e_1_3_2_1_23_1","volume-title":"mixup: Beyond empirical risk minimization,\" arXiv preprint arXiv:1710.09412","author":"Zhang H.","year":"2017","unstructured":"H. Zhang , M. Cisse , Y. N. Dauphin , and D. Lopez-Paz , \" mixup: Beyond empirical risk minimization,\" arXiv preprint arXiv:1710.09412 , 2017 . H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, \"mixup: Beyond empirical risk minimization,\" arXiv preprint arXiv:1710.09412, 2017."},{"key":"e_1_3_2_1_24_1","first-page":"21798","article-title":"Hard negative mixing for contrastive learning","volume":"33","author":"Kalantidis Y.","year":"2020","unstructured":"Y. Kalantidis , M. B. Sariyildiz , N. Pion , P. Weinzaepfel , and D. Larlus , \" Hard negative mixing for contrastive learning ,\" Advances in Neural Information Processing Systems , vol. 33 , pp. 21798 - 21809 , 2020 . Y. Kalantidis, M. B. Sariyildiz, N. Pion, P. Weinzaepfel, and D. Larlus, \"Hard negative mixing for contrastive learning,\" Advances in Neural Information Processing Systems, vol. 33, pp. 21798-21809, 2020.","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"CSSE 2022: 2022 5th International Conference on Computer Science and Software Engineering","acronym":"CSSE 2022","location":"Guilin China"},"container-title":["Proceedings of the 5th International Conference on Computer Science and Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3569966.3570002","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3569966.3570002","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:19Z","timestamp":1750182559000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3569966.3570002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":24,"alternative-id":["10.1145\/3569966.3570002","10.1145\/3569966"],"URL":"https:\/\/doi.org\/10.1145\/3569966.3570002","relation":{},"subject":[],"published":{"date-parts":[[2022,10,21]]},"assertion":[{"value":"2022-12-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}