{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:03:03Z","timestamp":1775815383054,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Hong Kong Research Grants Council","award":["PolyU 15200021 and PolyU 15207322"],"award-info":[{"award-number":["PolyU 15200021 and PolyU 15207322"]}]},{"name":"The InnoHK project"},{"name":"The Hong Kong Polytechnic University","award":["P0036200, P0042693, and P0043302"],"award-info":[{"award-number":["P0036200, P0042693, and P0043302"]}]},{"name":"NSFC","award":["62102335"],"award-info":[{"award-number":["62102335"]}]},{"name":"Key Laboratory of Smart Education of Guangdong Higher Education Institutes, Jinan University","award":["2022LSYS003"],"award-info":[{"award-number":["2022LSYS003"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583355","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T23:30:51Z","timestamp":1682551851000},"page":"3723-3733","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Fairly Adaptive Negative Sampling for Recommendations"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5804-0211","authenticated-orcid":false,"given":"Xiao","family":"Chen","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong and Center for Artificial Intelligence and Robotics, HKISI_CAS, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-1233","authenticated-orcid":false,"given":"Wenqi","family":"Fan","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7559-6924","authenticated-orcid":false,"given":"Jingfan","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong and Center for Artificial Intelligence and Robotics, HKISI_CAS, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5290-7163","authenticated-orcid":false,"given":"Haochen","family":"Liu","sequence":"additional","affiliation":[{"name":"Michigan State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0491-307X","authenticated-orcid":false,"given":"Zitao","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Institute of Smart Education, Jinan University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2648-3875","authenticated-orcid":false,"given":"Zhaoxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, China and Center for Artificial Intelligence and Robotics, HKISI_CAS, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-471X","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Multisided fairness for recommendation. arXiv preprint arXiv:1707.00093","author":"Burke Robin","year":"2017","unstructured":"Robin Burke. 2017. Multisided fairness for recommendation. arXiv preprint arXiv:1707.00093 (2017)."},{"key":"e_1_3_2_1_2_1","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. In arXiv preprint arXiv:2010.03240."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539359"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Ting Chen Yizhou Sun Yue Shi and Liangjie Hong. 2017. On sampling strategies for neural network-based collaborative filtering. In KDD.","DOI":"10.1145\/3097983.3098202"},{"key":"e_1_3_2_1_5_1","volume-title":"International conference on machine learning. PMLR, 2260\u20132268","author":"Cutkosky Ashok","year":"2020","unstructured":"Ashok Cutkosky and Harsh Mehta. 2020. Momentum improves normalized sgd. In International conference on machine learning. PMLR, 2260\u20132268."},{"key":"e_1_3_2_1_6_1","volume-title":"Simplify and robustify negative sampling for implicit collaborative filtering. NeurIPS","author":"Ding Jingtao","year":"2020","unstructured":"Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, and Depeng Jin. 2020. Simplify and robustify negative sampling for implicit collaborative filtering. NeurIPS (2020)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Tyler Derr Yao Ma Jianping Wang Jiliang Tang and Qing Li. 2019. Deep Adversarial Social Recommendation. In IJCAI.","DOI":"10.24963\/ijcai.2019\/187"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00140"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00056"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Qing Li and Min Cheng. 2018. Deep modeling of social relations for recommendation. In AAAI.","DOI":"10.1609\/aaai.v32i1.12132"},{"key":"e_1_3_2_1_11_1","volume-title":"Generative Diffusion Models on Graphs: Methods and Applications. arXiv preprint arXiv:2302.02591","author":"Fan Wenqi","year":"2023","unstructured":"Wenqi Fan, Chengyi Liu, Yunqing Liu, Jiatong Li, Hang Li, Hui Liu, Jiliang Tang, and Qing Li. 2023. Generative Diffusion Models on Graphs: Methods and Applications. arXiv preprint arXiv:2302.02591 (2023)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Xiaorui Liu Wei Jin Xiangyu Zhao Jiliang Tang and Qing Li. 2022. Graph Trend Filtering Networks for Recommendation. In SIGIR.","DOI":"10.1145\/3477495.3531985"},{"key":"e_1_3_2_1_13_1","volume-title":"A graph neural network framework for social recommendations. TKDE","author":"Fan Wenqi","year":"2020","unstructured":"Wenqi Fan, Yao Ma, Qing Li, Jianping Wang, Guoyong Cai, Jiliang Tang, and Dawei Yin. 2020. A graph neural network framework for social recommendations. TKDE (2020)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Dawei Yin Jianping Wang Jiliang Tang and Qing Li. 2019. Deep social collaborative filtering. In RecSys.","DOI":"10.1145\/3298689.3347011"},{"key":"e_1_3_2_1_15_1","volume-title":"A Comprehensive Survey on Trustworthy Recommender Systems. arXiv preprint arXiv:2209.10117","author":"Fan Wenqi","year":"2022","unstructured":"Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, 2022. A Comprehensive Survey on Trustworthy Recommender Systems. arXiv preprint arXiv:2209.10117 (2022)."},{"key":"e_1_3_2_1_16_1","unstructured":"Yingqiang Ge Shuchang Liu Ruoyuan Gao Yikun Xian Yunqi Li Xiangyu Zhao Changhua Pei Fei Sun Junfeng Ge Wenwu Ou 2021. Towards long-term fairness in recommendation. In WSDM."},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249\u2013256","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249\u2013256."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Elizabeth G\u00f3mez. 2020. Characterizing and Mitigating the Impact of Data Imbalance for Stakeholders in Recommender Systems. In RecSys.","DOI":"10.1145\/3383313.3411454"},{"key":"e_1_3_2_1_19_1","volume-title":"Equality of opportunity in supervised learning. Advances in neural information processing systems 29","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. Advances in neural information processing systems 29 (2016)."},{"key":"e_1_3_2_1_20_1","volume-title":"The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis)","author":"Harper F\u00a0Maxwell","year":"2015","unstructured":"F\u00a0Maxwell Harper and Joseph\u00a0A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) (2015)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_23_1","unstructured":"Toshihiro Kamishima and Shotaro Akaho. 2017. Considerations on recommendation independence for a find-good-items task. (2017)."},{"key":"e_1_3_2_1_24_1","volume-title":"Chi","author":"Lahoti Preethi","year":"2020","unstructured":"Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi. 2020. Fairness without demographics through adversarially reweighted learning. Advances in neural information processing systems 33 (2020), 728\u2013740."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Jurek Leonhardt Avishek Anand and Megha Khosla. 2018. User fairness in recommender systems. In WWW.","DOI":"10.1145\/3184558.3186949"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Jie Li Yongli Ren Ke Deng and et.al. 2022. FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback. In WWW.","DOI":"10.1145\/3485447.3511958"},{"key":"e_1_3_2_1_27_1","unstructured":"Yunqi Li Hanxiong Chen Zuohui Fu Yingqiang Ge and Yongfeng Zhang. 2021. User-oriented fairness in recommendation. In WWW."},{"key":"e_1_3_2_1_28_1","volume-title":"Self-supervised learning for fair recommender systems. Applied Soft Computing","author":"Liu Haifeng","year":"2022","unstructured":"Haifeng Liu, Hongfei Lin, Wenqi Fan, Yuqi Ren, Bo Xu, Xiaokun Zhang, Dongzhen Wen, Nan Zhao, Yuan Lin, and Liang Yang. 2022. Self-supervised learning for fair recommender systems. Applied Soft Computing (2022)."},{"key":"e_1_3_2_1_29_1","volume-title":"Trustworthy ai: A computational perspective. arXiv preprint arXiv:2107.06641","author":"Liu Haochen","year":"2021","unstructured":"Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil\u00a0K Jain, and Jiliang Tang. 2021. Trustworthy ai: A computational perspective. arXiv preprint arXiv:2107.06641 (2021)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.108058"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Julian McAuley Christopher Targett Qinfeng Shi and Anton Van Den\u00a0Hengel. 2015. Image-based recommendations on styles and substitutes. In SIGIR.","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314290"},{"key":"e_1_3_2_1_33_1","volume-title":"Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg\u00a0S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Marco Morik Ashudeep Singh Jessica Hong and Thorsten Joachims. 2020. Controlling fairness and bias in dynamic learning-to-rank. In SIGIR. 429\u2013438.","DOI":"10.1145\/3397271.3401100"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Dae\u00a0Hoon Park and Yi Chang. 2019. Adversarial sampling and training for semi-supervised information retrieval. In WWW.","DOI":"10.1145\/3308558.3313416"},{"key":"e_1_3_2_1_36_1","volume-title":"Fairrec: Two-sided fairness for personalized recommendations in two-sided platforms. In WWW.","author":"Patro K","year":"2020","unstructured":"Gourab\u00a0K Patro, Arpita Biswas, Niloy Ganguly, Krishna\u00a0P Gummadi, and Abhijnan Chakraborty. 2020. Fairrec: Two-sided fairness for personalized recommendations in two-sided platforms. In WWW."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle and Christoph Freudenthaler. 2014. Improving pairwise learning for item recommendation from implicit feedback. In WSDM.","DOI":"10.1145\/2556195.2556248"},{"key":"e_1_3_2_1_38_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618","author":"Rendle Steffen","year":"2012","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)."},{"key":"e_1_3_2_1_39_1","unstructured":"Yuji Roh Kangwook Lee Steven\u00a0Euijong Whang and Changho Suh. 2021. FairBatch: Batch Selection for Model Fairness. In ICLR."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Ashudeep Singh and Thorsten Joachims. 2018. Fairness of exposure in rankings. In KDD.","DOI":"10.1145\/3219819.3220088"},{"key":"e_1_3_2_1_41_1","unstructured":"Ilya Sutskever James Martens George Dahl and Geoffrey Hinton. 2013. On the importance of initialization and momentum in deep learning. In ICML. PMLR."},{"key":"e_1_3_2_1_42_1","volume-title":"Metaheuristics for bi-level optimization","author":"Talbi El-Ghazali","unstructured":"El-Ghazali Talbi. 2013. A taxonomy of metaheuristics for bi-level optimization. In Metaheuristics for bi-level optimization. Springer, 1\u201339."},{"key":"e_1_3_2_1_43_1","unstructured":"Jingru Tan Changbao Wang Buyu Li Quanquan Li Wanli Ouyang Changqing Yin and Junjie Yan. 2020. Equalization loss for long-tailed object recognition. In CVPR."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Qi Wan Xiangnan He Xiang Wang Jiancan Wu Wei Guo and Ruiming Tang. 2022. Cross Pairwise Ranking for Unbiased Item Recommendation. In WWW.","DOI":"10.1145\/3485447.3512010"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080786"},{"key":"e_1_3_2_1_46_1","unstructured":"Jiahao Wu Wenqi Fan Jingfan Chen Shengcai Liu Qing Li and Ke Tang. 2022. Disentangled contrastive learning for social recommendation. In CIKM."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269283"},{"key":"e_1_3_2_1_48_1","unstructured":"Guanhua Zhang Yihua Zhang Yang Zhang Wenqi Fan Qing Li Sijia Liu and Shiyu Chang. 2022. Fairness Reprogramming. In NeurIPS."},{"key":"e_1_3_2_1_49_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.","DOI":"10.1145\/2484028.2484126"},{"key":"e_1_3_2_1_50_1","volume-title":"Autoloss: Automated loss function search in recommendations. In KDD.","author":"Zhao Xiangyu","year":"2021","unstructured":"Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, and Chong Wang. 2021. Autoloss: Automated loss function search in recommendations. In KDD."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Ziwei Zhu Jianling Wang and James Caverlee. 2019. Improving top-k recommendation via jointcollaborative autoencoders. In WWW.","DOI":"10.1145\/3308558.3313678"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Ziwei Zhu Jianling Wang James Caverlee and et.al. 2020. Measuring and mitigating item under-recommendation bias in personalized ranking systems. In SIGIR.","DOI":"10.1145\/3397271.3401177"}],"event":{"name":"WWW '23: The ACM Web Conference 2023","location":"Austin TX USA","acronym":"WWW '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583355","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583355","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:23Z","timestamp":1750178243000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":52,"alternative-id":["10.1145\/3543507.3583355","10.1145\/3543507"],"URL":"https:\/\/doi.org\/10.1145\/3543507.3583355","relation":{},"subject":[],"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}