{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:41:40Z","timestamp":1776400900651,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"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":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557229","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"2148-2157","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation"],"prefix":"10.1145","author":[{"given":"Haolun","family":"Wu","sequence":"first","affiliation":[{"name":"McGill University, Montreal, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Ma","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong SAR, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Montreal, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Liu","sequence":"additional","affiliation":[{"name":"McGill University, Montreal, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiming","family":"Tang","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Coates","sequence":"additional","affiliation":[{"name":"McGill University, Montreal, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR","author":"Bertin-Mahieux Thierry","year":"2011","unstructured":"Thierry Bertin-Mahieux , Daniel P.W. Ellis , Brian Whitman , and Paul Lamere . 2011 . The Million Song Dataset . In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011). Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. 2011. The Million Song Dataset. In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011)."},{"key":"e_1_3_2_1_2_1","volume-title":"Proc. Int. Conf. on Machine Learning.","author":"Blanc Guy","year":"2018","unstructured":"Guy Blanc and Steffen Rendle . 2018 . Adaptive Sampled Softmax with Kernel Based Sampling . In Proc. Int. Conf. on Machine Learning. Guy Blanc and Steffen Rendle. 2018. Adaptive Sampled Softmax with Kernel Based Sampling. In Proc. Int. Conf. on Machine Learning."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380214"},{"key":"e_1_3_2_1_4_1","volume-title":"Proc. Conf. Knowledge Discovery and Data Mining.","author":"Chen Yihong","year":"2019","unstructured":"Yihong Chen , Bei Chen , Xiangnan He , Chen Gao , Yong Li , Jian-Guang Lou , and Yue Wang . 2019 . (\u03bb)Opt: Learn to Regularize Recommender Models in Finer Levels . In Proc. Conf. Knowledge Discovery and Data Mining. Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, and Yue Wang. 2019. (\u03bb)Opt: Learn to Regularize Recommender Models in Finer Levels. In Proc. Conf. Knowledge Discovery and Data Mining."},{"key":"e_1_3_2_1_5_1","unstructured":"Eunjoon Cho Seth A. Myers and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In KDD. ACM 1082--1090.  Eunjoon Cho Seth A. Myers and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In KDD. ACM 1082--1090."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3186905"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/309"},{"key":"e_1_3_2_1_8_1","volume-title":"Proc. Int. Joint Conf. on Artificial Intelligence.","author":"Elkan Charles","year":"2001","unstructured":"Charles Elkan . 2001 . The Foundations of Cost-Sensitive Learning . In Proc. Int. Joint Conf. on Artificial Intelligence. Charles Elkan. 2001. The Foundations of Cost-Sensitive Learning. In Proc. Int. Joint Conf. on Artificial Intelligence."},{"key":"e_1_3_2_1_9_1","volume-title":"Proc. Conf. Knowledge Discovery and Data Mining Cup.","author":"Gantner Zeno","year":"2012","unstructured":"Zeno Gantner , Lucas Drumond , Christoph Freudenthaler , and Lars Schmidt-Thieme . 2012 . Personalized Ranking for Non-Uniformly Sampled Items . In Proc. Conf. Knowledge Discovery and Data Mining Cup. Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2012. Personalized Ranking for Non-Uniformly Sampled Items. In Proc. Conf. Knowledge Discovery and Data Mining Cup."},{"key":"e_1_3_2_1_10_1","volume-title":"Proc. Int. Conf. on Artificial Intelligence and Statistics.","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio . 2010 . Understanding the difficulty of training deep feed forward neural networks . In Proc. Int. Conf. on Artificial Intelligence and Statistics. Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feed forward neural networks. In Proc. Int. Conf. on Artificial Intelligence and Statistics."},{"key":"e_1_3_2_1_11_1","volume-title":"McAuley","author":"He Ruining","year":"2016","unstructured":"Ruining He and Julian J . McAuley . 2016 . Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In WWW. ACM , 507--517. Ruining He and Julian J. McAuley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In WWW. ACM, 507--517."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911489"},{"key":"e_1_3_2_1_15_1","unstructured":"Yifan Hu Yehuda Koren and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In ICDM.  Yifan Hu Yehuda Koren and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In ICDM."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"e_1_3_2_1_17_1","volume-title":"Ferdous Ahmed Sohel, and Roberto Togneri","author":"Khan Salman H.","year":"2018","unstructured":"Salman H. Khan , Munawar Hayat , Mohammed Bennamoun , Ferdous Ahmed Sohel, and Roberto Togneri . 2018 . Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data. IEEE Trans. Neural Networks Learn. Syst . (2018). Salman H. Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous Ahmed Sohel, and Roberto Togneri. 2018. Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data. IEEE Trans. Neural Networks Learn. Syst. (2018)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556220"},{"key":"e_1_3_2_1_19_1","volume-title":"Matrix Factorization Techniques for Recommender Systems. Computer","author":"Koren Yehuda","year":"2009","unstructured":"Yehuda Koren , Robert M. Bell , and Chris Volinsky . 2009. Matrix Factorization Techniques for Recommender Systems. Computer ( 2009 ). Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer (2009)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380187"},{"key":"e_1_3_2_1_21_1","volume-title":"Proc. Int. Conf. World Wide Web.","author":"Liang Dawen","unstructured":"Dawen Liang , Laurent Charlin , James McInerney , and David M. Blei . 2016. Modeling User Exposure in Recommendation . In Proc. Int. Conf. World Wide Web. Dawen Liang, Laurent Charlin, James McInerney, and David M. Blei. 2016. Modeling User Exposure in Recommendation. In Proc. Int. Conf. World Wide Web."},{"key":"e_1_3_2_1_22_1","volume-title":"Proc. Conf. on Research and Development in Information Retrieval.","author":"Liu Chao","unstructured":"Chao Liu , Ryen W. White , and Susan T. Dumais . 2010. Understanding web browsing behaviors through Weibull analysis of dwell time . In Proc. Conf. on Research and Development in Information Retrieval. Chao Liu, Ryen W. White, and Susan T. Dumais. 2010. Understanding web browsing behaviors through Weibull analysis of dwell time. In Proc. Conf. on Research and Development in Information Retrieval."},{"key":"e_1_3_2_1_23_1","volume-title":"Proc. Int. Conf. on Learning Representations (Poster).","author":"Liu Hanxiao","year":"2019","unstructured":"Hanxiao Liu , Karen Simonyan , and Yiming Yang . 2019 b. DARTS: Differentiable Architecture Search . In Proc. Int. Conf. on Learning Representations (Poster). Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019b. DARTS: Differentiable Architecture Search. In Proc. Int. Conf. on Learning Representations (Poster)."},{"key":"e_1_3_2_1_24_1","unstructured":"Shikun Liu Andrew J. Davison and Edward Johns. 2019a. Self-Supervised Generalisation with Meta Auxiliary Learning. In NeurIPS.  Shikun Liu Andrew J. Davison and Edward Johns. 2019a. Self-Supervised Generalisation with Meta Auxiliary Learning. In NeurIPS."},{"key":"e_1_3_2_1_25_1","unstructured":"Hongyu Lu Min Zhang and Shaoping Ma. 2018. Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading. In SIGIR. ACM.  Hongyu Lu Min Zhang and Shaoping Ma. 2018. Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading. In SIGIR. ACM."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331247"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290977"},{"key":"e_1_3_2_1_28_1","volume-title":"Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation. Computing Research Repository","author":"Ma Chen","year":"2021","unstructured":"Chen Ma , Liheng Ma , Yingxue Zhang , Ruiming Tang , Xue Liu , and Mark Coates . 2021a. Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation. Computing Research Repository ( 2021 ). Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, and Mark Coates. 2021a. Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation. Computing Research Repository (2021)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16553"},{"key":"e_1_3_2_1_30_1","volume-title":"One-Class Collaborative Filtering. In IEEE Int. Conf. on Data Mining.","author":"Pan Rong","year":"2008","unstructured":"Rong Pan , Yunhong Zhou , Bin Cao , Nathan Nan Liu , Rajan M. Lukose , Martin Scholz , and Qiang Yang . 2008 . One-Class Collaborative Filtering. In IEEE Int. Conf. on Data Mining. Rong Pan, Yunhong Zhou, Bin Cao, Nathan Nan Liu, Rajan M. Lukose, Martin Scholz, and Qiang Yang. 2008. One-Class Collaborative Filtering. In IEEE Int. Conf. on Data Mining."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124313"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556248"},{"key":"e_1_3_2_1_33_1","volume-title":"Proc. Conf. Uncertainty in Artificial Intelligence.","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2009 a. BPR: Bayesian Personalized Ranking from Implicit Feedback . In Proc. Conf. Uncertainty in Artificial Intelligence. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009a. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proc. Conf. Uncertainty in Artificial Intelligence."},{"key":"e_1_3_2_1_34_1","volume-title":"Proc. Conf. on Uncertainty in Artificial Intelligence.","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2009 b. BPR: Bayesian Personalized Ranking from Implicit Feedback . In Proc. Conf. on Uncertainty in Artificial Intelligence. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009b. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proc. Conf. on Uncertainty in Artificial Intelligence."},{"key":"e_1_3_2_1_35_1","volume-title":"Proc. Int. Conf. on Machine Learning.","author":"Salakhutdinov Ruslan","unstructured":"Ruslan Salakhutdinov , Andriy Mnih , and Geoffrey E. Hinton . 2007. Restricted Boltzmann machines for collaborative filtering . In Proc. Int. Conf. on Machine Learning. Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey E. Hinton. 2007. Restricted Boltzmann machines for collaborative filtering. In Proc. Int. Conf. on Machine Learning."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_1_37_1","volume-title":"Proc. Int. Conf. on Machine Learning.","author":"Shen Yanyao","year":"2019","unstructured":"Yanyao Shen and Sujay Sanghavi . 2019 . Learning with Bad Training Data via Iterative Trimmed Loss Minimization . In Proc. Int. Conf. on Machine Learning. Yanyao Shen and Sujay Sanghavi. 2019. Learning with Bad Training Data via Iterative Trimmed Loss Minimization. In Proc. Int. Conf. on Machine Learning."},{"key":"e_1_3_2_1_38_1","volume-title":"Proc. Adv. Neural Inf. Proc. Systems.","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 . In Proc. Adv. Neural Inf. Proc. Systems. 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. In Proc. Adv. Neural Inf. Proc. Systems."},{"key":"e_1_3_2_1_39_1","volume-title":"Multi-Graph Convolution Collaborative Filtering. In IEEE Int. Conf. on Data Mining.","author":"Sun Jianing","year":"2019","unstructured":"Jianing Sun , Yingxue Zhang , Chen Ma , Mark Coates , Huifeng Guo , Ruiming Tang , and Xiuqiang He . 2019 a. Multi-Graph Convolution Collaborative Filtering. In IEEE Int. Conf. on Data Mining. Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, and Xiuqiang He. 2019a. Multi-Graph Convolution Collaborative Filtering. In IEEE Int. Conf. on Data Mining."},{"key":"e_1_3_2_1_40_1","volume-title":"Multi-graph Convolution Collaborative Filtering","author":"Sun Jianing","unstructured":"Jianing Sun , Yingxue Zhang , Chen Ma , Mark Coates , Huifeng Guo , Ruiming Tang , and Xiuqiang He. 2019b. Multi-graph Convolution Collaborative Filtering . In ICDM. IEEE , 1306--1311. Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, and Xiuqiang He. 2019b. Multi-graph Convolution Collaborative Filtering. In ICDM. IEEE, 1306--1311."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271730"},{"key":"e_1_3_2_1_42_1","unstructured":"Laurens van der Maaten and Geoffrey E. Hinton. [n.d.]. Visualizing Data using t-SNE. Journal of Machine Learning Research ([n. d.]).  Laurens van der Maaten and Geoffrey E. Hinton. [n.d.]. Visualizing Data using t-SNE. Journal of Machine Learning Research ([n. d.])."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Chun Wang Shirui Pan Ruiqi Hu Guodong Long Jing Jiang and Chengqi Zhang. 2019b. Attributed Graph Clustering: A Deep Attentional Embedding Approach. In IJCAI.  Chun Wang Shirui Pan Ruiqi Hu Guodong Long Jing Jiang and Chengqi Zhang. 2019b. Attributed Graph Clustering: A Deep Attentional Embedding Approach. In IJCAI.","DOI":"10.24963\/ijcai.2019\/509"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441800"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380098"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347037"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835837"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2645710.2645724"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401155"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2484028.2484126"}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557229","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557229","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:07Z","timestamp":1750182547000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":51,"alternative-id":["10.1145\/3511808.3557229","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557229","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}