{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:53:06Z","timestamp":1773881586095,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T00:00:00Z","timestamp":1694649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["62272262, 61972223, U1936217, and U20B2060"],"award-info":[{"award-number":["62272262, 61972223, U1936217, and U20B2060"]}]},{"name":"the National Key Research and Development Program of China","award":["2022YFB3104702"],"award-info":[{"award-number":["2022YFB3104702"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,9,14]]},"DOI":"10.1145\/3604915.3608814","type":"proceedings-article","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T22:40:23Z","timestamp":1694731223000},"page":"540-550","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5274-5205","authenticated-orcid":false,"given":"Yunzhu","family":"Pan","sequence":"first","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7561-5646","authenticated-orcid":false,"given":"Chen","family":"Gao","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7886-9238","authenticated-orcid":false,"given":"Jianxin","family":"Chang","sequence":"additional","affiliation":[{"name":"Beijing Kuaishou Technology Co., Ltd., China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8662-3696","authenticated-orcid":false,"given":"Yanan","family":"Niu","sequence":"additional","affiliation":[{"name":"Beijing Kuaishou Technology Co., Ltd., China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1714-5527","authenticated-orcid":false,"given":"Yang","family":"Song","sequence":"additional","affiliation":[{"name":"Beijing Kuaishou Technology Co., Ltd., China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3636-3618","authenticated-orcid":false,"given":"Kun","family":"Gai","sequence":"additional","affiliation":[{"name":"Unaffiliated, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0419-5514","authenticated-orcid":false,"given":"Depeng","family":"Jin","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-1659","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1033"},{"key":"e_1_3_2_1_2_1","volume-title":"Microsoft Recommenders: Best Practices for Production-Ready Recommendation Systems. In Companion Proceedings of the Web Conference","author":"Argyriou Andreas","year":"2020","unstructured":"Andreas Argyriou, Miguel Gonz\u00e1lez-Fierro, and Le Zhang. 2020. Microsoft Recommenders: Best Practices for Production-Ready Recommendation Systems. In Companion Proceedings of the Web Conference 2020. 50\u201351."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Yue Cao XiaoJiang Zhou Jiaqi Feng Peihao Huang Yao Xiao Dayao Chen and Sheng Chen. 2022. Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction. In CIKM.","DOI":"10.1145\/3511808.3557082"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403344"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532073"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462968"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462919"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3475768","article-title":"Multi-interest diversification for end-to-end sequential recommendation","volume":"40","author":"Chen Wanyu","year":"2021","unstructured":"Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, and Maarten De\u00a0Rijke. 2021. Multi-interest diversification for end-to-end sequential recommendation. ACM Transactions on Information Systems (TOIS) 40, 1 (2021), 1\u201330.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449947"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Jingtao Ding Yuhan Quan Xiangnan He Yong Li and Depeng Jin. 2019. Reinforced Negative Sampling for Recommendation with Exposure Data.. In IJCAI. Macao 2230\u20132236.","DOI":"10.24963\/ijcai.2019\/309"},{"key":"e_1_3_2_1_11_1","first-page":"1094","article-title":"Simplify and robustify negative sampling for implicit collaborative filtering","volume":"33","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. Advances in Neural Information Processing Systems 33 (2020), 1094\u20131105.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3568022"},{"key":"e_1_3_2_1_13_1","volume-title":"Causal Inference in Recommender Systems: A Survey and Future Directions. arXiv preprint arXiv:2208.12397","author":"Gao Chen","year":"2022","unstructured":"Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, and Yong Li. 2022. Causal Inference in Recommender Systems: A Survey and Future Directions. arXiv preprint arXiv:2208.12397 (2022)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532040"},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Machine Learning. PMLR, 3953\u20133963","author":"Gupta Shantanu","year":"2021","unstructured":"Shantanu Gupta, Hao Wang, Zachary Lipton, and Yuyang Wang. 2021. Correcting exposure bias for link recommendation. In International Conference on Machine Learning. PMLR, 3953\u20133963."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911489"},{"key":"e_1_3_2_1_18_1","volume-title":"Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939","author":"Hidasi Bal\u00e1zs","year":"2015","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102142"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401072"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_22_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma P","year":"2014","unstructured":"Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357814"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132926"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371786"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5945"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470948"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412744"},{"key":"e_1_3_2_1_29_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_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_1_31_1","volume-title":"Dynamic routing between capsules. Advances in neural information processing systems 30","author":"Sabour Sara","year":"2017","unstructured":"Sara Sabour, Nicholas Frosst, and Geoffrey\u00a0E Hinton. 2017. Dynamic routing between capsules. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_32_1","volume-title":"SiReN: Sign-Aware Recommendation Using Graph Neural Networks","author":"Seo Changwon","year":"2022","unstructured":"Changwon Seo, Kyeong-Joong Jeong, Sungsu Lim, and Won-Yong Shin. 2022. SiReN: Sign-Aware Recommendation Using Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems (2022)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_1_34_1","volume-title":"Measuring and testing dependence by correlation of distances. The annals of statistics 35, 6","author":"Sz\u00e9kely J","year":"2007","unstructured":"G\u00e1bor\u00a0J Sz\u00e9kely, Maria\u00a0L Rizzo, and Nail\u00a0K Bakirov. 2007. Measuring and testing dependence by correlation of distances. The annals of statistics 35, 6 (2007), 2769\u20132794."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532081"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512092"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512082"},{"key":"e_1_3_2_1_39_1","volume-title":"A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation","author":"Wu Le","year":"2022","unstructured":"Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2022. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462972"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/349"},{"key":"e_1_3_2_1_42_1","volume-title":"IJCAI, Vol.\u00a017.","author":"Xue Hong-Jian","unstructured":"Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep matrix factorization models for recommender systems.. In IJCAI, Vol.\u00a017. Melbourne, Australia, 3203\u20133209."},{"key":"e_1_3_2_1_43_1","volume-title":"Self-propagation graph neural network for recommendation","author":"Yu Wenhui","year":"2021","unstructured":"Wenhui Yu, Xiao Lin, Jinfei Liu, Junfeng Ge, Wenwu Ou, and Zheng Qin. 2021. Self-propagation graph neural network for recommendation. IEEE Transactions on Knowledge and Data Engineering (2021)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Zeping Yu Jianxun Lian Ahmad Mahmoody Gongshen Liu and Xing Xie. 2019. Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation.. In IJCAI. 4213\u20134219.","DOI":"10.24963\/ijcai.2019\/585"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx105"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512098"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Yu Zhu Hao Li Yikang Liao Beidou Wang Ziyu Guan Haifeng Liu and Deng Cai. 2017. What to Do Next: Modeling User Behaviors by Time-LSTM.. In IJCAI Vol.\u00a017. 3602\u20133608.","DOI":"10.24963\/ijcai.2017\/504"}],"event":{"name":"RecSys '23: Seventeenth ACM Conference on Recommender Systems","location":"Singapore Singapore","acronym":"RecSys '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGAI ACM Special Interest Group on Artificial Intelligence","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval","SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGecom Special Interest Group on Economics and Computation"]},"container-title":["Proceedings of the 17th ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604915.3608814","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3604915.3608814","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:07Z","timestamp":1750178767000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604915.3608814"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,14]]},"references-count":49,"alternative-id":["10.1145\/3604915.3608814","10.1145\/3604915"],"URL":"https:\/\/doi.org\/10.1145\/3604915.3608814","relation":{},"subject":[],"published":{"date-parts":[[2023,9,14]]},"assertion":[{"value":"2023-09-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}