{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T18:05:33Z","timestamp":1770228333156,"version":"3.49.0"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"China National Science Foundation","award":["62202282 and 62072301"],"award-info":[{"award-number":["62202282 and 62072301"]}]},{"name":"Program of Technology Innovation of the Science and Technology Commission of Shanghai Municipality","award":["21511104700"],"award-info":[{"award-number":["21511104700"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>\n            A\n            <jats:bold>session-based recommendation system (SRS)<\/jats:bold>\n            tries to predict the next possible choice of anonymous users. In recent years,\n            <jats:bold>graph neural network (GNN)<\/jats:bold>\n            models have been successfully applied to SRSs and have achieved great success. Using GNN models in SRSs, each session graph is processed successively to obtain the embedding of the node (i.e, each action on an item), which is then imported into the prediction module to generate recommendation results. However, solely depending on the session graph to obtain the node embeddings is not sufficient because each session only involves a few items. Therefore, neighbor sessions have been used to extend the session graph to learn more informative node representations. In this paper, we introduce a\n            <jats:italic>S<\/jats:italic>\n            ession-based recommendation\n            <jats:italic>MO<\/jats:italic>\n            del based on\n            <jats:italic>N<\/jats:italic>\n            eighbor sessions with similar probabilistic int\n            <jats:italic>E<\/jats:italic>\n            ntions(SMONE). SMONE models the intentions behind sessions in a probabilistic way and retrieves the neighbor sessions with similar intentions. After the neighbor sessions are found, the target session and its neighbor sessions are modeled as a hypyergraph to learn the contextualized embeddings, which are combined with item embeddings through GNN to produce the final item recommendations. Experiments on real-world datasets prove the effectiveness and superiority of SMONE.\n          <\/jats:p>","DOI":"10.1145\/3587099","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T10:57:12Z","timestamp":1678359432000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["SMONE: A Session-based Recommendation Model Based on Neighbor Sessions with Similar Probabilistic Intentions"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8023-6117","authenticated-orcid":false,"given":"Bohan","family":"Jia","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0036-9436","authenticated-orcid":false,"given":"Jian","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7775-1740","authenticated-orcid":false,"given":"Shiyou","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6146-9887","authenticated-orcid":false,"given":"Nengjun","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2523-9971","authenticated-orcid":false,"given":"Xin","family":"Dong","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7744-7789","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2186-699X","authenticated-orcid":false,"given":"Lei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6682-1448","authenticated-orcid":false,"given":"Linjian","family":"Mo","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.06.077"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210129"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCONS.2017.8250563"},{"key":"e_1_3_1_5_2","first-page":"29","volume-title":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","author":"Bertini Marco","year":"2020","unstructured":"Marco Bertini, Andrea Ferracani, Riccardo Papucci, and Alberto Del Bimbo. 2020. Keeping up with the influencers: Improving user recommendation in Instagram using visual content. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 29\u201334."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/81.3.541"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403170"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00035"},{"key":"e_1_3_1_10_2","article-title":"Intention adaptive graph neural network for category-aware session-based recommendation","author":"Cui Chuan","year":"2021","unstructured":"Chuan Cui, Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Zhenwei Dong, and Zhihua Wei. 2021. Intention adaptive graph neural network for category-aware session-based recommendation. arXiv preprint arXiv:2112.15352 (2021).","journal-title":"arXiv preprint arXiv:2112.15352"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/INISTA.2019.8778352"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330839"},{"key":"e_1_3_1_14_2","article-title":"NISER: Normalized item and session representations to handle popularity bias","author":"Gupta Priyanka","year":"2019","unstructured":"Priyanka Gupta, Diksha Garg, Pankaj Malhotra, Lovekesh Vig, and Gautam Shroff. 2019. NISER: Normalized item and session representations to handle popularity bias. arXiv preprint arXiv:1909.04276 (2019).","journal-title":"arXiv preprint arXiv:1909.04276"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_1_16_2","article-title":"Session-based recommendations with recurrent neural networks","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).","journal-title":"arXiv preprint arXiv:1511.06939"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/312624.312649"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/su11123336"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109872"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3050571"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132926"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_22"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0135"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219950"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487331"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1023\/a:1007692713085"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3490479"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401273"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108894"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358010"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109896"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_1_34_2","article-title":"Sequential recommendation with user evolving preference decomposition","author":"Shao Weiqi","year":"2022","unstructured":"Weiqi Shao, Xu Chen, Long Xia, Jiashu Zhao, and Dawei Yin. 2022. Sequential recommendation with user evolving preference decomposition. arXiv preprint arXiv:2203.16942 (2022).","journal-title":"arXiv preprint arXiv:2203.16942"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3497748"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.elerap.2019.100879"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109282"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331210"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-021-00930-2"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3465401"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71246-8_18"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269302"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401142"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-020-00135-z"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507215"},{"key":"e_1_3_1_47_2","article-title":"Self-supervised hypergraph convolutional networks for session-based recommendation","author":"Xia Xin","year":"2020","unstructured":"Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, and Xiangliang Zhang. 2020. Self-supervised hypergraph convolutional networks for session-based recommendation. arXiv preprint arXiv:2012.06852 (2020).","journal-title":"arXiv preprint arXiv:2012.06852"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/547"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/2488388.2488514"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2914683"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401319"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.10.028"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20161-5_34"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW51313.2020.00041"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371840"},{"issue":"2","key":"e_1_3_1_56_2","first-page":"1","article-title":"Learning a hierarchical intent model for next-item recommendation","volume":"40","author":"Zhu Nengjun","year":"2021","unstructured":"Nengjun Zhu, Jian Cao, Xinjiang Lu, and Hui Xiong. 2021. Learning a hierarchical intent model for next-item recommendation. ACM Trans. Inf. Syst. 40, 2 (2021), 1\u201328.","journal-title":"ACM Trans. Inf. Syst."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3587099","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3587099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:01Z","timestamp":1750183681000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3587099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,12]]},"references-count":55,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,9,30]]}},"alternative-id":["10.1145\/3587099"],"URL":"https:\/\/doi.org\/10.1145\/3587099","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,12]]},"assertion":[{"value":"2022-06-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-27","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}