{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:15:22Z","timestamp":1780766122976,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Center for Computer Network?Shenzhen?Ministry of Education"},{"name":"Beijing Academy of Artificial Intelligence (BAAI), the Natural Science Foundation of Guangdong Province","award":["2023A1515012914"],"award-info":[{"award-number":["2023A1515012914"]}]},{"name":"Major Key Project of PCL for Experiments and Applications","award":["PCL2021A06"],"award-info":[{"award-number":["PCL2021A06"]}]},{"name":"Basic Research Fund of Shenzhen City","award":["JCYJ20210324120012033; JSGG20210802154402007"],"award-info":[{"award-number":["JCYJ20210324120012033; JSGG20210802154402007"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276154"],"award-info":[{"award-number":["62276154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,7,19]]},"DOI":"10.1145\/3539618.3592021","type":"proceedings-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:22:23Z","timestamp":1689726143000},"page":"2174-2178","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Mining Interest Trends and Adaptively Assigning Sample Weight for Session-based Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0884-529X","authenticated-orcid":false,"given":"Kai","family":"Ouyang","sequence":"first","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2447-4107","authenticated-orcid":false,"given":"Xianghong","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3518-9555","authenticated-orcid":false,"given":"Miaoxin","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2362-0214","authenticated-orcid":false,"given":"Zuotong","family":"Xie","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5128-5649","authenticated-orcid":false,"given":"Hai-Tao","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7465-1082","authenticated-orcid":false,"given":"Shuangyong","family":"Song","sequence":"additional","affiliation":[{"name":"China Telecom Corporation Ltd. &amp; Data&amp;AI Technology Company, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6985-6105","authenticated-orcid":false,"given":"Yu","family":"Zhao","sequence":"additional","affiliation":[{"name":"China Telecom Corporation Ltd. &amp; Data&amp;AI Technology Company, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Bal\u00e1zs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In CIKM. 843--852.","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_2_1_2_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_3_1","doi-asserted-by":"crossref","unstructured":"Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In RecSys. 306--310.","DOI":"10.1145\/3109859.3109872"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_5_1","volume-title":"Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings.","author":"Diederik","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Jing Li Pengjie Ren Zhumin Chen Zhaochun Ren Tao Lian and Jun Ma. 2017. Neural attentive session-based recommendation. In CIKM. 1419--1428.","DOI":"10.1145\/3132847.3132926"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Qiao Liu Yifu Zeng Refuoe Mokhosi and Haibin Zhang. 2018. STAMP: short-term attention\/memory priority model for session-based recommendation. In SIGKDD. 1831--1839.","DOI":"10.1145\/3219819.3219950"},{"key":"e_1_3_2_1_9_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-emnlp.159"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3382764"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Ruihong Qiu Jingjing Li Zi Huang and Hongzhi Yin. 2019. Rethinking the item order in session-based recommendation with graph neural networks. In CIKM. 579--588.","DOI":"10.1145\/3357384.3358010"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle Christoph Freudenthaler and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. 811--820.","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988452"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Ziyang Wang Wei Wei Gao Cong Xiao-Li Li Xian-Ling Mao and Minghui Qiu. 2020. Global context enhanced graph neural networks for session-based recommendation. In SIGIR. 169--178.","DOI":"10.1145\/3397271.3401142"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Xin Xia Hongzhi Yin Junliang Yu Qinyong Wang Lizhen Cui and Xiangliang Zhang. 2021. Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation. In AAAI. 4503--4511.","DOI":"10.1609\/aaai.v35i5.16578"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Chengfeng Xu Pengpeng Zhao Yanchi Liu Victor S Sheng Jiajie Xu Fuzhen Zhuang Junhua Fang and Xiaofang Zhou. 2019. Graph Contextualized Self-Attention Network for Session-based Recommendation.. In IJCAI. 3940--3946.","DOI":"10.24963\/ijcai.2019\/547"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557645"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Fajie Yuan Alexandros Karatzoglou Ioannis Arapakis Joemon M Jose and Xiangnan He. 2019. A simple convolutional generative network for next item recommendation. In WSDM. 582--590.","DOI":"10.1145\/3289600.3290975"}],"event":{"name":"SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Taipei Taiwan","acronym":"SIGIR '23","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3592021","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3539618.3592021","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:49Z","timestamp":1750178269000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3592021"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,18]]},"references-count":20,"alternative-id":["10.1145\/3539618.3592021","10.1145\/3539618"],"URL":"https:\/\/doi.org\/10.1145\/3539618.3592021","relation":{},"subject":[],"published":{"date-parts":[[2023,7,18]]},"assertion":[{"value":"2023-07-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}