{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:36:00Z","timestamp":1773902160981,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the CCCD Key Lab of Ministry of Culture and Tourism"},{"name":"the National Natural Science Foundation of China","award":["62272437 and 62121002"],"award-info":[{"award-number":["62272437 and 62121002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3645371","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"3253-3264","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2616-6880","authenticated-orcid":false,"given":"Wentao","family":"Shi","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8665-6953","authenticated-orcid":false,"given":"Chenxu","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5828-9842","authenticated-orcid":false,"given":"Fuli","family":"Feng","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7863-5183","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5199-1428","authenticated-orcid":false,"given":"Wenjie","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6663-926X","authenticated-orcid":false,"given":"Junkang","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8472-7992","authenticated-orcid":false,"given":"Xiangnan","family":"He","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Convex Optimization","author":"Boyd Stephen","unstructured":"Stephen Boyd and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge University Press."},{"key":"e_1_3_2_2_2_1","volume-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition","author":"Bradley Andrew P","year":"1997","unstructured":"Andrew P Bradley. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, Vol. 30, 7 (1997), 1145--1159."},{"key":"e_1_3_2_2_3_1","volume-title":"Rich-Item Recommendations for Rich-Users via GCNN: Exploiting Dynamic and Static Side Information. CoRR","author":"Budhiraja Amar","year":"2020","unstructured":"Amar Budhiraja, Gaurush Hiranandani, Navya Yarrabelly, Ayush Choure, Oluwasanmi Koyejo, and Prateek Jain. 2020. Rich-Item Recommendations for Rich-Users via GCNN: Exploiting Dynamic and Static Side Information. CoRR, Vol. abs\/2001.10495 (2020)."},{"key":"e_1_3_2_2_4_1","volume-title":"Learning to Rank with Nonsmooth Cost Functions","author":"Burges Christopher J. C.","unstructured":"Christopher J. C. Burges, Robert Ragno, and Quoc Viet Le. 2006. Learning to Rank with Nonsmooth Cost Functions. In NIPS. MIT Press, 193--200."},{"key":"e_1_3_2_2_5_1","volume-title":"Bias and Debias in Recommender System: A Survey and Future Directions. CoRR","author":"Chen Jiawei","year":"2020","unstructured":"Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020. Bias and Debias in Recommender System: A Survey and Future Directions. CoRR, Vol. abs\/2010.03240 (2020)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. ACM 191--198.","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/1541-0420.00071"},{"key":"e_1_3_2_2_8_1","unstructured":"Yanbo Fan Siwei Lyu Yiming Ying and Bao-Gang Hu. 2017. Learning with Average Top-k Loss. In NIPS. 497--505."},{"key":"e_1_3_2_2_9_1","volume-title":"On the Consistency of AUC Pairwise Optimization","author":"Gao Wei","unstructured":"Wei Gao and Zhi-Hua Zhou. 2015. On the Consistency of AUC Pairwise Optimization. In IJCAI. AAAI Press, 939--945."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-1531-9"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Yuan Gao Xiang Wang Xiangnan He Zhenguang Liu Huamin Feng and Yongdong Zhang. 2023 b. Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum. In WWW. ACM 1528--1538.","DOI":"10.1145\/3543507.3583268"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Yuan Gao Xiang Wang Xiangnan He Zhenguang Liu Huamin Feng and Yongdong Zhang. 2023 c. Alleviating Structural Distribution Shift in Graph Anomaly Detection. In WSDM. ACM 357--365.","DOI":"10.1145\/3539597.3570377"},{"key":"e_1_3_2_2_13_1","volume-title":"Deep Sparse Rectifier Neural Networks. In AISTATS (JMLR Proceedings","volume":"323","author":"Glorot Xavier","year":"2011","unstructured":"Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep Sparse Rectifier Neural Networks. In AISTATS (JMLR Proceedings, Vol. 15). JMLR.org, 315--323."},{"key":"e_1_3_2_2_14_1","volume-title":"\u00d6 zlem \u00d6 zg\u00f6 bek, and Xiaomeng Su","author":"Gulla Jon Atle","year":"2017","unstructured":"Jon Atle Gulla, Lemei Zhang, Peng Liu, \u00d6 zlem \u00d6 zg\u00f6 bek, and Xiaomeng Su. 2017. The Adressa dataset for news recommendation. In WI. ACM, 1042--1048."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"e_1_3_2_2_16_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."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Xiangnan He Kuan Deng Xiang Wang Yan Li Yong-Dong Zhang and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR. ACM 639--648.","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_2_18_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. ACM 173--182.","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_2_19_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"4270","author":"Hiranandani Gaurush","year":"2020","unstructured":"Gaurush Hiranandani, Warut Vijitbenjaronk, Sanmi Koyejo, and Prateek Jain. 2020. Optimization and Analysis of the pAp@k Metric for Recommender Systems. In ICML (Proceedings of Machine Learning Research, Vol. 119). PMLR, 4260--4270."},{"key":"e_1_3_2_2_20_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"5871","author":"Kumar Abhishek","year":"2021","unstructured":"Abhishek Kumar, Harikrishna Narasimhan, and Andrew Cotter. 2021. Implicit rate-constrained optimization of non-decomposable objectives. In ICML (Proceedings of Machine Learning Research, Vol. 139). PMLR, 5861--5871."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Kelong Mao Jieming Zhu Jinpeng Wang Quanyu Dai Zhenhua Dong Xi Xiao and Xiuqiang He. 2021. SimpleX: A Simple and Strong Baseline for Collaborative Filtering. In CIKM. ACM 1243--1252.","DOI":"10.1145\/3459637.3482297"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X8900900307"},{"key":"e_1_3_2_2_23_1","volume-title":"ICML (1) (JMLR Workshop and Conference Proceedings","author":"Narasimhan Harikrishna","unstructured":"Harikrishna Narasimhan and Shivani Agarwal. 2013a. A Structural SVM Based Approach for Optimizing Partial AUC. In ICML (1) (JMLR Workshop and Conference Proceedings, Vol. 28). JMLR.org, 516--524."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Harikrishna Narasimhan and Shivani Agarwal. 2013b. SVM(_pAUC )(^tight ): a new support vector method for optimizing partial AUC based on a tight convex upper bound. In KDD. ACM 167--175.","DOI":"10.1145\/2487575.2487674"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00972"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Hang Pan Jiawei Chen Fuli Feng Wentao Shi Junkang Wu and Xiangnan He. 2023. Discriminative-Invariant Representation Learning for Unbiased Recommendation. In IJCAI. ijcai.org 2270--2278.","DOI":"10.24963\/ijcai.2023\/252"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Yash Patel Giorgos Tolias and Jir'i Matas. 2022. Recall@k Surrogate Loss with Large Batches and Similarity Mixup. In CVPR. IEEE 7492--7501.","DOI":"10.1109\/CVPR52688.2022.00735"},{"key":"e_1_3_2_2_28_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"18152","author":"Qiu Zi-Hao","year":"2022","unstructured":"Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, and Tianbao Yang. 2022. Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. In ICML (Proceedings of Machine Learning Research, Vol. 162). PMLR, 18122--18152."},{"key":"e_1_3_2_2_29_1","volume-title":"BPR: Bayesian Personalized Ranking from Implicit Feedback","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI. AUAI Press, 452--461."},{"key":"e_1_3_2_2_30_1","unstructured":"Huiyang Shao Qianqian Xu Zhiyong Yang Shilong Bao and Qingming Huang. 2022. Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm. In NeurIPS."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Wentao Shi Jiawei Chen Fuli Feng Jizhi Zhang Junkang Wu Chongming Gao and Xiangnan He. 2023. On the Theories Behind Hard Negative Sampling for Recommendation. In WWW. ACM 812--822.","DOI":"10.1145\/3543507.3583223"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557274"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Wenjie Wang Fuli Feng Xiangnan He Liqiang Nie and Tat-Seng Chua. 2021. Denoising Implicit Feedback for Recommendation. In WSDM. ACM 373--381.","DOI":"10.1145\/3437963.3441800"},{"key":"e_1_3_2_2_34_1","volume-title":"Diffusion Recommender Model. CoRR","author":"Wang Wenjie","year":"2023","unstructured":"Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, and Tat-Seng Chua. 2023. Diffusion Recommender Model. CoRR, Vol. abs\/2304.04971 (2023)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Xuanhui Wang Cheng Li Nadav Golbandi Michael Bendersky and Marc Najork. 2018. The LambdaLoss Framework for Ranking Metric Optimization. In CIKM. ACM 1313--1322.","DOI":"10.1145\/3269206.3271784"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3145690"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280217718866"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240381"},{"key":"e_1_3_2_2_39_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"11829","author":"Yang Zhiyong","year":"2021","unstructured":"Zhiyong Yang, Qianqian Xu, Shilong Bao, Yuan He, Xiaochun Cao, and Qingming Huang. 2021. When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC. In ICML (Proceedings of Machine Learning Research, Vol. 139). PMLR, 11820--11829."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3185311"},{"key":"e_1_3_2_2_41_1","unstructured":"Yao Yao Qihang Lin and Tianbao Yang. 2022. Large-scale Optimization of Partial AUC in a Range of False Positive Rates. In NeurIPS."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Weinan Zhang Tianqi Chen Jun Wang and Yong Yu. 2013. Optimizing top-n collaborative filtering via dynamic negative item sampling. In SIGIR. ACM 785--788.","DOI":"10.1145\/2484028.2484126"},{"key":"e_1_3_2_2_43_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"27573","author":"Zhu Dixian","year":"2022","unstructured":"Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, and Tianbao Yang. 2022. When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee. In ICML (Proceedings of Machine Learning Research, Vol. 162). PMLR, 27548--27573."}],"event":{"name":"WWW '24: The ACM Web Conference 2024","location":"Singapore Singapore","acronym":"WWW '24","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645371","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3645371","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:29:09Z","timestamp":1755822549000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645371"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":43,"alternative-id":["10.1145\/3589334.3645371","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3645371","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}