{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T11:00:02Z","timestamp":1760785202343,"version":"3.44.0"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"5","funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2021QD014, 2024JJ039, and 2024TD001"],"award-info":[{"award-number":["2021QD014, 2024JJ039, and 2024TD001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372057"],"award-info":[{"award-number":["62372057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>Session-Based Recommendation (SBR) systems, traditionally reliant on complex Graph Neural Networks (GNNs), often face challenges with marginal performance improvements despite increased model complexity. In this article, we dissect the classical GNN-based SBR models and empirically find that the sophisticated GNN propagations might be redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we introduce Atten-Mixer+, an advanced iteration of our previously developed Multi-Level Attention Mixture Network (Atten-Mixer). Atten-Mixer+ forgoes GNN propagation in favor of a dynamic and adaptive readout process, tailored to the unique characteristics of each session. Different from the vanilla version, Atten-Mixer+ features the Adaptive Intent Scaler (AIS) layer, which dynamically determines the depth of multi-level user intent analysis and a soft allocation approach for generating user intent queries across entire user interaction sequences. This innovative design allows Atten-Mixer+ to capture a nuanced and comprehensive understanding of user behaviors, overcoming the limitations of fixed-length analysis. Empirical evaluations on benchmark datasets highlight Atten-Mixer+\u2019s superior efficiency and effectiveness, marking a significant step forward in the predictive accuracy of SBR systems.<\/jats:p>","DOI":"10.1145\/3700445","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T11:13:42Z","timestamp":1729077222000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Advancing Session-Based Recommendations with Atten-Mixer+: Dynamic and Adaptive Multi-Level Intent Mining"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8691-1846","authenticated-orcid":false,"given":"Peiyan","family":"Zhang","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-1153","authenticated-orcid":false,"given":"Jiayan","family":"Guo","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9867-1712","authenticated-orcid":false,"given":"Chaozhuo","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7715-8519","authenticated-orcid":false,"given":"Liying","family":"Kang","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5451-6617","authenticated-orcid":false,"given":"Jaeboum","family":"Kim","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3708-2823","authenticated-orcid":false,"given":"Jie","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing Foreign Studies University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2111-7385","authenticated-orcid":false,"given":"Xi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fuzhou University, Fuzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4003-0290","authenticated-orcid":false,"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1826-4069","authenticated-orcid":false,"given":"Haohan","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4478-9720","authenticated-orcid":false,"given":"Sunghun","family":"Kim","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109912"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-018-9654-y"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3564284"},{"key":"e_1_3_2_5_2","first-page":"994","volume-title":"Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM \u201919).","author":"Chen Tianwen","year":"2019","unstructured":"Tianwen Chen and Raymond Chi-Wing Wong. 2019. Session-based recommendation with local invariance. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM \u201919). IEEE, 994\u2013999."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403170"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657928"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450005"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864770"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2013.120"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3501396"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-014-1599-8"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557314"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330839"},{"key":"e_1_3_2_15_2","unstructured":"Priyanka Gupta Diksha Garg Pankaj Malhotra Lovekesh Vig and Gautam Shroff. 2019. NISER: Normalized item and session representations to handle popularity bias. arXiv:1909.04276. Retrieved from https:\/\/arxiv.org\/abs\/1909.04276"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482071"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330838"},{"issue":"4","key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"552","DOI":"10.5127\/jep.017711","article-title":"Multi-level models of information processing, and their application to psychosis","volume":"3","author":"Heriot-Maitland Charles","year":"2012","unstructured":"Charles Heriot-Maitland. 2012. Multi-level models of information processing, and their application to psychosis. Journal of Experimental Psychopathology 3, 4 (2012), 552\u2013571.","journal-title":"Journal of Experimental Psychopathology"},{"key":"e_1_3_2_19_2","volume-title":"Proceedings of the 4th International Conference on Learning Representations","author":"Hidasi Bal\u00e1zs","year":"2016","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, L. Baltrunas, and D. Tikk. 2016. Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations. Retrieved from https:\/\/arxiv.org\/abs\/1511.06939"},{"key":"e_1_3_2_20_2","first-page":"9377","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Huang Zhongyu","year":"2022","unstructured":"Zhongyu Huang, Yingheng Wang, Chaozhuo Li, and Huiguang He. 2022. Going deeper into permutation-sensitive graph neural networks. In Proceedings of the 39th International Conference on Machine Learning, 9377\u20139409."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1080\/09548980701418942"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101906"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583526"},{"key":"e_1_3_2_24_2","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_25_2","first-page":"2575","article-title":"Variational dropout and the local reparameterization trick","volume":"28","author":"Kingma Durk P.","year":"2015","unstructured":"Durk P. Kingma, Tim Salimans, and Max Welling. 2015. Variational dropout and the local reparameterization trick. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 28, 2575\u20132583.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_26_2","first-page":"223","article-title":"AdsGNN: Behavior-graph augmented relevance modeling in sponsored search","author":"Li Chaozhuo","year":"2021","unstructured":"Chaozhuo Li, Bochen Pang, Yuming Liu, Hao Sun, Zheng Liu, Xing Xie, Tianqi Yang, Yanling Cui, Liangjie Zhang, and Qi Zhang. 2021. AdsGNN: Behavior-graph augmented relevance modeling in sponsored search. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 223\u2013232.","journal-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132926"},{"key":"e_1_3_2_28_2","volume-title":"Proceedings of the 4th International Conference on Learning Representations","author":"Li Yujia","year":"2015","unstructured":"Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. In Proceedings of the 4th International Conference on Learning Representations. Retrieved from https:\/\/arxiv.org\/abs\/1511.05493"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219950"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412222"},{"key":"e_1_3_2_31_2","first-page":"2498","volume-title":"Proceedings of the International Conference on Machine Learning.","author":"Molchanov Dmitry","year":"2017","unstructured":"Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov. 2017. Variational dropout sparsifies deep neural networks. In Proceedings of the International Conference on Machine Learning. PMLR, 2498\u20132507."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412014"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/CNSI.2011.72"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358010"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014806"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1145\/1772690.1772773","article-title":"Factorizing personalized Markov chains for next-basket recommendation","author":"Rendle S.","year":"2010","unstructured":"S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web (WWW \u201910), 811\u2013820.","journal-title":"Proceedings of the 19th International Conference on World Wide Web (WWW \u201910)"},{"issue":"43","key":"e_1_3_2_37_2","first-page":"1265","article-title":"An MDP-based recommender system","volume":"6","author":"Shani Guy","year":"2005","unstructured":"Guy Shani, D. Heckerman, and R. Brafman. 2005. An MDP-based recommender system. Journal of Machine Learning Research 6, 43 (2005), 1265\u20131295.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_38_2","unstructured":"R. Srivastava Klaus Greff and J. Schmidhuber. 2015. Highway networks. arXiv:1505.00387. Retrieved from https:\/\/arxiv.org\/abs\/1505.00387"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_2_40_2","first-page":"6000","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 30, 6000\u20136010."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401133"},{"key":"e_1_3_2_42_2","first-page":"1","volume-title":"Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME \u201920)","author":"Wang Jinshan","year":"2020","unstructured":"Jinshan Wang, Qianfang Xu, Jiahuan Lei, Chaoqun Lin, and Bo Xiao. 2020. PA-GGAN: Session-based recommendation with position-aware gated graph attention network. In Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME \u201920), 1\u20136."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3465401"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591663"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512083"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401142"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401142"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16578"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/547"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614803"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401319"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290975"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16593"},{"key":"e_1_3_2_55_2","first-page":"3394","article-title":"Deep sets","volume":"30","author":"Zaheer Manzil","year":"2017","unstructured":"Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R. Salakhutdinov, and Alexander J. Smola. 2017. Deep sets. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 30, 3394\u20133404.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_56_2","unstructured":"Peiyan Zhang and Sunghun Kim. 2023. A survey on incremental update for neural recommender systems. arXiv:2303.02851. Retrieved from https:\/\/arxiv.org\/abs\/2303.02851"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657721"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449788"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3700445","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T01:57:16Z","timestamp":1755568636000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3700445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,18]]},"references-count":57,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10,31]]}},"alternative-id":["10.1145\/3700445"],"URL":"https:\/\/doi.org\/10.1145\/3700445","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"type":"print","value":"2157-6904"},{"type":"electronic","value":"2157-6912"}],"subject":[],"published":{"date-parts":[[2025,8,18]]},"assertion":[{"value":"2024-01-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}