{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T20:15:12Z","timestamp":1783541712960,"version":"3.55.0"},"reference-count":205,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users\u2019 short-term preferences and aims at providing a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.<\/jats:p>","DOI":"10.1145\/3696413","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T11:26:33Z","timestamp":1726658793000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Graph and Sequential Neural Networks in Session-based Recommendation: A Survey"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1802-4284","authenticated-orcid":false,"given":"Zihao","family":"Li","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3763-5080","authenticated-orcid":false,"given":"Chao","family":"Yang","sequence":"additional","affiliation":[{"name":"Ocean University of China, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8331-3410","authenticated-orcid":false,"given":"Yakun","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9582-3445","authenticated-orcid":false,"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7963-8813","authenticated-orcid":false,"given":"Hongxu","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia and The Education University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4149-839X","authenticated-orcid":false,"given":"Lina","family":"Yao","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3326-4147","authenticated-orcid":false,"given":"Michael","family":"Sheng","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","author":"Adomavicius Gediminas","year":"2005","unstructured":"Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6 (2005), 734\u2013749.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_3_2","first-page":"1007","volume-title":"Proceedings of the 17th ACM Conference on Recommender Systems","author":"Bao Keqin","year":"2023","unstructured":"Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems. 1007\u20131014."},{"key":"e_1_3_2_4_2","unstructured":"Yoshua Bengio R\u00e9jean Ducharme Pascal Vincent and Christian Janvin. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3 null (March 2003) 1137\u20131155."},{"key":"e_1_3_2_5_2","volume-title":"Proceedings of the ICLR","author":"Bojchevski Aleksandar","year":"2018","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. In Proceedings of the ICLR."},{"key":"e_1_3_2_6_2","unstructured":"Antoine Bordes Nicolas Usunier Alberto Garcia-Duran Jason Weston and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems 26 (2013)."},{"key":"e_1_3_2_7_2","first-page":"141","volume-title":"Proceedings of the SP","author":"Bourtoule Lucas","year":"2021","unstructured":"Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021. Machine unlearning. In Proceedings of the SP. 141\u2013159."},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Diego Carraro and Derek Bridge. 2024. Enhancing recommendation diversity by re-ranking with large language models. arXiv preprint arXiv:2401.11506 (2024).","DOI":"10.1145\/3700604"},{"key":"e_1_3_2_9_2","first-page":"603","volume-title":"Proceedings of the Recommender Systems Handbook","author":"Castells Pablo","year":"2021","unstructured":"Pablo Castells, Neil Hurley, and Saul Vargas. 2021. Novelty and diversity in recommender systems. In Proceedings of the Recommender Systems Handbook. 603\u2013646."},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Oscar Celma Herrada. 2009. Music recommendation and discovery in the long tail. Universitat Pompeu Fabra.","DOI":"10.1007\/978-3-642-13287-2"},{"key":"e_1_3_2_11_2","volume-title":"Proceedings of the SIGIR","author":"Chen Chen","year":"2021","unstructured":"Chen Chen, Jie Guo, and Bin Song. 2021. Dual attention transfer in session-based recommendation with multi-dimensional integration. In Proceedings of the SIGIR."},{"key":"e_1_3_2_12_2","unstructured":"Hanxiong Chen Xu Chen Shaoyun Shi and Yongfeng Zhang. 2021. Generate natural language explanations for recommendation. ArXiv preprint (2021)."},{"key":"e_1_3_2_13_2","article-title":"Bias and debias in recommender system: A survey and future directions","author":"Chen Jiawei","year":"2023","unstructured":"Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems 41, 3 (2023), 1\u201339.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","unstructured":"Jinpeng Chen Haiyang Li Xudong Zhang Fan Zhang Senzhang Wang Kaimin Wei and Jiaqi Ji. 2023. SR-HetGNN: session-based recommendation with heterogeneous graph neural network. Knowl. Inf. Syst. 66 2 (Sept. 2023) 1111\u20131134. 10.1007\/s10115-023-01986-4","DOI":"10.1007\/s10115-023-01986-4"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","unstructured":"Minghao Chen and Jiale Zheng. 2021. Incorporating adjacent user modeling into session-based recommendation with graph neural networks. In 2021 International Conference on Data Mining Workshops (ICDMW). 1\u20139. 10.1109\/ICDMW53433.2021.00008","DOI":"10.1109\/ICDMW53433.2021.00008"},{"key":"e_1_3_2_16_2","volume-title":"Proceedings of the SIGIR","author":"Chen Qian","year":"2023","unstructured":"Qian Chen, Zhiqiang Guo, Jianjun Li, and Guohui Li. 2023. Knowledge-enhanced multi-view graph neural networks for session-based recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_17_2","first-page":"3793","volume-title":"Proceedings of the CIKM","author":"Chen Qian","year":"2023","unstructured":"Qian Chen, Jianjun Li, Zhiqiang Guo, Guohui Li, and Zhiying Deng. 2023. Attribute-enhanced dual channel representation learning for session-based recommendation. In Proceedings of the CIKM. 3793\u20133797."},{"key":"e_1_3_2_18_2","volume-title":"Proceedings of the ICDM","author":"Chen Tianwen","year":"2019","unstructured":"Tianwen Chen and Raymond Chi-Wing Wong. 2019. Session-based recommendation with local invariance. In Proceedings of the ICDM."},{"key":"e_1_3_2_19_2","volume-title":"Proceedings of the KDD","author":"Chen Tianwen","year":"2020","unstructured":"Tianwen Chen and Raymond Chi-Wing Wong. 2020. Handling information loss of graph neural networks for session-based recommendation. In Proceedings of the KDD."},{"key":"e_1_3_2_20_2","volume-title":"Proceedings of the WSDM","author":"Chen Tianwen","year":"2021","unstructured":"Tianwen Chen and Raymond Chi-Wing Wong. 2021. An efficient and effective framework for session-based social recommendation. In Proceedings of the WSDM."},{"key":"e_1_3_2_21_2","volume-title":"Proceedings of the CIKM","author":"Chen Wanyu","year":"2019","unstructured":"Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. 2019. A dynamic co-attention network for session-based recommendation. In Proceedings of the CIKM."},{"key":"e_1_3_2_22_2","first-page":"765","volume-title":"Proceedings of the SIGIR","author":"Chen Xu","year":"2019","unstructured":"Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2019. Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation. In Proceedings of the SIGIR. 765\u2013774."},{"key":"e_1_3_2_23_2","unstructured":"Xiaocong Chen Lina Yao Julian McAuley Guanglin Zhou and Xianzhi Wang. 2021. A survey of deep reinforcement learning in recommender systems: A systematic review and future directions. arXiv:2109.03540. Retrieved from https:\/\/arxiv.org\/abs\/2109.03540"},{"key":"e_1_3_2_24_2","first-page":"64","volume-title":"Proceedings of the ICONIP","author":"Chen Yakun","year":"2022","unstructured":"Yakun Chen, Zihao Li, Chao Yang, Xianzhi Wang, Guodong Long, and Guandong Xu. 2022. Adaptive graph recurrent network for multivariate time series imputation. In Proceedings of the ICONIP. Springer, 64\u201373."},{"key":"e_1_3_2_25_2","unstructured":"Yakun Chen Kaize Shi Zhangkai Wu Juan Chen Xianzhi Wang Julian McAuley Guandong Xu and Shui Yu. 2024. Temporal disentangled contrastive diffusion model for spatiotemporal imputation. arXiv preprint arXiv:2402.11558 (2024)."},{"key":"e_1_3_2_26_2","first-page":"150","volume-title":"Proceedings of the WSDM","author":"Choi Minjin","year":"2022","unstructured":"Minjin Choi, Jinhong Kim, Joonseok Lee, Hyunjung Shim, and Jongwuk Lee. 2022. S-Walk: Accurate and scalable session-based recommendation with random walks. In Proceedings of the WSDM. 150\u2013160."},{"key":"e_1_3_2_27_2","first-page":"659","volume-title":"Proceedings of the SIGIR","author":"Clarke Charles L. A.","year":"2008","unstructured":"Charles L. A. Clarke, Maheedhar Kolla, Gordon V. Cormack, Olga Vechtomova, Azin Ashkan, Stefan B\u00fcttcher, and Ian MacKinnon. 2008. Novelty and diversity in information retrieval evaluation. In Proceedings of the SIGIR. 659\u2013666."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","unstructured":"Alexis Conneau Douwe Kiela Holger Schwenk Lo\u00efc Barrault and Antoine Bordes. 2017. Supervised learning of universal sentence representations from natural language inference data. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing Martha Palmer Rebecca Hwa and Sebastian Riedel (Eds.). Association for Computational Linguistics Copenhagen Denmark 670\u2013680. 10.18653\/v1\/D17-1070","DOI":"10.18653\/v1\/D17-1070"},{"key":"e_1_3_2_29_2","unstructured":"Emile Contal and Garrin McGoldrick. 2024. RAGSys: Item-cold-start recommender as RAG system. arXiv preprint arXiv:2405.17587 (2024)."},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion models in vision: A survey","author":"Croitoru Florinel-Alin","year":"2023","unstructured":"Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. 2023. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 9 (2023), 10850\u201310869.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_31_2","volume-title":"Proceedings of the DASFAA","author":"Cui Chuan","year":"2022","unstructured":"Chuan Cui, Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Hanning Gao, and Zhihua Wei. 2022. Intention adaptive graph neural network for category-aware session-based recommendation. In Proceedings of the DASFAA."},{"key":"e_1_3_2_32_2","unstructured":"Zeyu Cui Jianxin Ma Chang Zhou Jingren Zhou and Hongxia Yang. 2022. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084 (2022)."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","unstructured":"James Davidson Benjamin Liebald Junning Liu Palash Nandy Taylor Van Vleet Ullas Gargi Sujoy Gupta Yu He Mike Lambert Blake Livingston and Dasarathi Sampath. 2010. The YouTube video recommendation system. In Proceedings of the Fourth ACM Conference on Recommender Systems (Barcelona Spain) (RecSys\u201910). Association for Computing Machinery New York NY USA 293\u2013296. 10.1145\/1864708.1864770","DOI":"10.1145\/1864708.1864770"},{"key":"e_1_3_2_34_2","volume-title":"Proceedings of the RecSys","author":"Moreira Gabriel de Souza Pereira","year":"2021","unstructured":"Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, and Even Oldridge. 2021. Transformers4Rec: Bridging the gap between NLP and sequential\/session-based recommendation. In Proceedings of the RecSys."},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","unstructured":"Yashar Deldjoo Zhankui He Julian McAuley Anton Korikov Scott Sanner Arnau Ramisa Ren\u00e9 Vidal Maheswaran Sathiamoorthy Atoosa Kasirzadeh and Silvia Milano. 2024. A review of modern recommender systems using generative models (Gen-RecSys). In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Barcelona Spain) (KDD\u201924). Association for Computing Machinery New York NY USA 6448\u20136458. 10.1145\/3637528.3671474","DOI":"10.1145\/3637528.3671474"},{"key":"e_1_3_2_36_2","volume-title":"Proceedings of the AAAI","author":"Deng Ailin","year":"2021","unstructured":"Ailin Deng and Bryan Hooi. 2021. Graph neural network-based anomaly detection in multivariate time series. In Proceedings of the AAAI."},{"key":"e_1_3_2_37_2","article-title":"G^ 3SR: Global graph guided session-based recommendation","author":"Deng Zhi-Hong","year":"2022","unstructured":"Zhi-Hong Deng, Chang-Dong Wang, Ling Huang, Jian-Huang Lai, and S. Yu Philip. 2022. G^ 3SR: Global graph guided session-based recommendation. IEEE Transactions on Neural Networks and Learning Systems 34, 12 (2022), 9671\u20139684.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_38_2","volume-title":"Proceedings of the SIGIR","author":"Ebesu Travis","year":"2018","unstructured":"Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative memory network for recommendation systems. In Proceedings of the SIGIR."},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","unstructured":"Ehsan Elahi Sajid Anwar Babar Shah Zahid Halim Abrar Ullah Imad Rida and Muhammad Waqas. 2024. Knowledge graph enhanced contextualized attention-based network for responsible user-specific recommendation. ACM Trans. Intell. Syst. Technol. 15 4 Article 83 (July 2024) 24 pages. 10.1145\/3641288","DOI":"10.1145\/3641288"},{"key":"e_1_3_2_40_2","volume-title":"Proceedings of the CIKM","author":"Fan Ziwei","year":"2021","unstructured":"Ziwei Fan, Zhiwei Liu, Shen Wang, Lei Zheng, and Philip S. Yu. 2021. Modeling sequences as distributions with uncertainty for sequential recommendation. In Proceedings of the CIKM."},{"key":"e_1_3_2_41_2","first-page":"2036","volume-title":"Proceedings of the WWW","author":"Fan Ziwei","year":"2022","unstructured":"Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, and Philip S. Yu. 2022. Sequential recommendation via stochastic self-attention. In Proceedings of the WWW. 2036\u20132047."},{"key":"e_1_3_2_42_2","article-title":"Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations","author":"Fang Hui","year":"2020","unstructured":"Hui Fang, Danning Zhang, Yiheng Shu, and Guibing Guo. 2020. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Transactions on Information Systems (TOIS\u201920) 39, 1 (2020), 1\u201342.","journal-title":"ACM Transactions on Information Systems (TOIS\u201920)"},{"key":"e_1_3_2_43_2","first-page":"69","volume-title":"Proceedings of the SIGIR","author":"Fu Zuohui","year":"2020","unstructured":"Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, and Gerard de Melo. 2020. Fairness-aware explainable recommendation over knowledge graphs. In Proceedings of the SIGIR. 69\u201378."},{"key":"e_1_3_2_44_2","unstructured":"Yunfan Gao Yun Xiong Xinyu Gao Kangxiang Jia Jinliu Pan Yuxi Bi Yi Dai Jiawei Sun and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997 (2023)."},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1145\/3523227.3546767","volume-title":"Proceedings of the 16th ACM Conference on Recommender Systems","author":"Geng Shijie","year":"2022","unstructured":"Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. Recommendation as language processing (rlp): A unified pretrain, personalized prompt and predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems. 299\u2013315."},{"key":"e_1_3_2_46_2","volume-title":"Proceedings of the KDD","author":"Grover Aditya","year":"2016","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the KDD."},{"key":"e_1_3_2_47_2","volume-title":"Proceedings of the SIGIR","author":"Guo Cheng","year":"2020","unstructured":"Cheng Guo, Mengfei Zhang, Jinyun Fang, Jiaqi Jin, and Mao Pan. 2020. Session-based recommendation with hierarchical leaping networks. In Proceedings of the SIGIR."},{"key":"e_1_3_2_48_2","volume-title":"Proceedings of the WSDM","author":"Guo Jiayan","year":"2022","unstructured":"Jiayan Guo, Yaming Yang, Xiangchen Song, Yuan Zhang, Yujing Wang, Jing Bai, and Yan Zhang. 2022. Learning multi-granularity consecutive user intent unit for session-based recommendation. In Proceedings of the WSDM."},{"key":"e_1_3_2_49_2","volume-title":"Proceedings of the CIKM","author":"Guo Jiayan","year":"2022","unstructured":"Jiayan Guo, Peiyan Zhang, Chaozhuo Li, Xing Xie, Yan Zhang, and Sunghun Kim. 2022. Evolutionary preference learning via graph nested GRU ODE for session-based recommendation. In Proceedings of the CIKM."},{"key":"e_1_3_2_50_2","volume-title":"Proceedings of the KDD","author":"Guo Lei","year":"2019","unstructured":"Lei Guo, Hongzhi Yin, Qinyong Wang, Tong Chen, Alexander Zhou, and Nguyen Quoc Viet Hung. 2019. Streaming session-based recommendation. In Proceedings of the KDD."},{"key":"e_1_3_2_51_2","unstructured":"William L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach California USA) (NIPS\u201917). Curran Associates Inc. Red Hook NY USA 1025\u20131035."},{"key":"e_1_3_2_52_2","volume-title":"Proceedings of the SIGIR","author":"Han Qilong","year":"2022","unstructured":"Qilong Han, Chi Zhang, Rui Chen, Riwei Lai, Hongtao Song, and Li Li. 2022. Multi-faceted global item relation learning for session-based recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_53_2","volume-title":"Proceedings of the 24th ACM International on Conference on Information and Knowledge Management","author":"He Shizhu","year":"2015","unstructured":"Shizhu He, Kang Liu, Guoliang Ji, and Jun Zhao. 2015. Learning to represent knowledge graphs with gaussian embedding. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management."},{"key":"e_1_3_2_54_2","volume-title":"Proceedings of the CIKM","author":"Hidasi Bal\u00e1zs","year":"2018","unstructured":"Bal\u00e1zs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the CIKM."},{"key":"e_1_3_2_55_2","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_56_2","first-page":"1796","volume-title":"Proceedings of the SIGIR","author":"Hou Yupeng","year":"2022","unstructured":"Yupeng Hou, Binbin Hu, Zhiqiang Zhang, and Wayne Xin Zhao. 2022. Core: Simple and effective session-based recommendation within consistent representation space. In Proceedings of the SIGIR. 1796\u20131801."},{"key":"e_1_3_2_57_2","first-page":"364","volume-title":"Proceedings of the European Conference on Information Retrieval","author":"Hou Yupeng","year":"2024","unstructured":"Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024. Large language models are zero-shot rankers for recommender systems. In Proceedings of the European Conference on Information Retrieval. 364\u2013381."},{"key":"e_1_3_2_58_2","unstructured":"Edward J. Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Lu Wang and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv:2106.09685. Retrieved from https:\/\/arxiv.org\/abs\/2106.09685"},{"key":"e_1_3_2_59_2","volume-title":"Proceedings of the IJCAI","author":"Hu Liang","year":"2017","unstructured":"Liang Hu, Longbing Cao, Shoujin Wang, Guandong Xu, Jian Cao, and Zhiping Gu. 2017. Diversifying personalized recommendation with user-session context. In Proceedings of the IJCAI."},{"key":"e_1_3_2_60_2","volume-title":"Proceedings of the AAAI","author":"Huang Chao","year":"2021","unstructured":"Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, and Jimmy Xiangji Huang. 2021. Graph-enhanced multi-task learning of multi-level transition dynamics for session-based recommendation. In Proceedings of the AAAI."},{"key":"e_1_3_2_61_2","doi-asserted-by":"crossref","unstructured":"Akshay Jagatap Nikki Gupta Sachin Farfade and Prakash Mandayam Comar. 2023. AttriBERT: Session-based product attribute recommendation with BERT. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 3421\u20133425.","DOI":"10.1145\/3539618.3594714"},{"key":"e_1_3_2_62_2","volume-title":"Proceedings of the RecSys","author":"Jannach Dietmar","year":"2017","unstructured":"Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the RecSys."},{"key":"e_1_3_2_63_2","article-title":"SMONE: A session-based recommendation model based on neighbor sessions with similar probabilistic intentions","author":"Jia Bohan","year":"2023","unstructured":"Bohan Jia, Jian Cao, Shiyou Qian, Nengjun Zhu, Xin Dong, Liang Zhang, Lei Cheng, and Linjian Mo. 2023. SMONE: A session-based recommendation model based on neighbor sessions with similar probabilistic intentions. ACM Transactions on Knowledge Discovery from Data 17, 8 (2023), 1\u201322.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_64_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117921","article-title":"Graph neural network for traffic forecasting: A survey","author":"Jiang Weiwei","year":"2022","unstructured":"Weiwei Jiang and Jiayun Luo. 2022. Graph neural network for traffic forecasting: A survey. Expert Systems with Applications 207 (2022), 117921.","journal-title":"Expert Systems with Applications"},{"key":"e_1_3_2_65_2","volume-title":"Proceedings of the ICDM","author":"Kang Wang-Cheng","year":"2018","unstructured":"Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the ICDM."},{"key":"e_1_3_2_66_2","volume-title":"Proceedings of the ICLR","author":"Kipf Thomas N.","year":"2022","unstructured":"Thomas N. Kipf and Max Welling. 2022. Semi-supervised classification with graph convolutional networks. In Proceedings of the ICLR."},{"key":"e_1_3_2_67_2","article-title":"Optimal and autonomous control using reinforcement learning: A survey","author":"Kiumarsi Bahare","year":"2017","unstructured":"Bahare Kiumarsi, Kyriakos G. Vamvoudakis, Hamidreza Modares, and Frank L. Lewis. 2017. Optimal and autonomous control using reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems 29, 6 (2017), 2042\u20132062.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_68_2","unstructured":"Takeshi Kojima Shixiang Shane Gu Machel Reid Yutaka Matsuo and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems 35 (2022) 22199\u201322213."},{"key":"e_1_3_2_69_2","volume-title":"Proceedings of the SIGIR","author":"Lai Siqi","year":"2022","unstructured":"Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang, and Aixin Sun. 2022. An Attribute-Driven Mirror Graph Network for Session-based Recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_70_2","article-title":"Disentangled graph neural networks for session-based recommendation","author":"Li Ansong","year":"2022","unstructured":"Ansong Li, Zhiyong Cheng, Fan Liu, Zan Gao, Weili Guan, and Yuxin Peng. 2022. Disentangled graph neural networks for session-based recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 8 (2022), 7870\u20137882.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_71_2","volume-title":"Proceedings of the ICANN","author":"Li Dan","year":"2019","unstructured":"Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, and See-Kiong Ng. 2019. MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In Proceedings of the ICANN."},{"key":"e_1_3_2_72_2","unstructured":"Junying Li Deng Cai and Xiaofei He. 2017. Learning graph-level representation for drug discovery. arXiv preprint arXiv:1709.03741 (2017)."},{"key":"e_1_3_2_73_2","volume-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","author":"Li Jing","year":"2017","unstructured":"Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management."},{"key":"e_1_3_2_74_2","first-page":"4947","volume-title":"Proceedings of the ACL","author":"Li Lei","year":"2021","unstructured":"Lei Li, Yongfeng Zhang, and Li Chen. 2021. Personalized transformer for explainable recommendation. In Proceedings of the ACL. 4947\u20134957."},{"key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3580488","article-title":"Personalized prompt learning for explainable recommendation","author":"Li Lei","year":"2023","unstructured":"Lei Li, Yongfeng Zhang, and Li Chen. 2023. Personalized prompt learning for explainable recommendation. ACM Transactions on Information Systems (2023), 1\u201326.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_76_2","first-page":"967","volume-title":"Proceedings of the CIKM","author":"Li Yang","year":"2021","unstructured":"Yang Li, Tong Chen, Peng-Fei Zhang, and Hongzhi Yin. 2021. Lightweight self-attentive sequential recommendation. In Proceedings of the CIKM. 967\u2013977."},{"key":"e_1_3_2_77_2","volume-title":"Proceedings of the CIKM","author":"Li Yinfeng","year":"2022","unstructured":"Yinfeng Li, Chen Gao, Xiaoyi Du, Huazhou Wei, Hengliang Luo, Depeng Jin, and Yong Li. 2022. Spatiotemporal-aware session-based recommendation with graph neural networks. In Proceedings of the CIKM."},{"key":"e_1_3_2_78_2","volume-title":"Proceedings of the SIGIR","author":"Li Yinfeng","year":"2022","unstructured":"Yinfeng Li, Chen Gao, Hengliang Luo, Depeng Jin, and Yong Li. 2022. Enhancing hypergraph neural networks with intent disentanglement for session-based recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_79_2","article-title":"Diffurec: A diffusion model for sequential recommendation","author":"Li Zihao","year":"2023","unstructured":"Zihao Li, Aixin Sun, and Chenliang Li. 2023. Diffurec: A diffusion model for sequential recommendation. ACM Transactions on Information Systems 42, 3 (2023), 1\u201328.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_80_2","volume-title":"Proceedings of the WSDM","author":"Li Zihao","year":"2023","unstructured":"Zihao Li, Xianzhi Wang, Chao Yang, Lina Yao, Julian McAuley, and Guandong Xu. 2023. Exploiting explicit and implicit item relationships for session-based recommendation. In Proceedings of the WSDM."},{"key":"e_1_3_2_81_2","first-page":"616","volume-title":"Proceedings of the WISE","author":"Li Zihao","year":"2022","unstructured":"Zihao Li, Xianzhi Wang, Lina Yao, Yakun Chen, Guandong Xu, and Ee-Peng Lim. 2022. Graph neural network with self-attention and multi-task learning for credit default risk prediction. In Proceedings of the WISE. Springer, 616\u2013629."},{"issue":"3","key":"e_1_3_2_82_2","doi-asserted-by":"crossref","first-page":"103619","DOI":"10.1016\/j.ipm.2023.103619","article-title":"Disentangle interest trend and diversity for sequential recommendation","volume":"61","author":"Li Zihao","year":"2024","unstructured":"Zihao Li, Yunfan Xie, Wei Emma Zhang, Pengfei Wang, Lixin Zou, Fei Li, Xiangyang Luo, and Chenliang Li. 2024. Disentangle interest trend and diversity for sequential recommendation. Information Processing and Management 61, 3 (2024), 103619.","journal-title":"Information Processing and Management"},{"key":"e_1_3_2_83_2","volume-title":"Proceedings of the RecSys","author":"Liang Dawen","year":"2016","unstructured":"Dawen Liang, Jaan Altosaar, Laurent Charlin, and David M. Blei. 2016. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In Proceedings of the RecSys."},{"key":"e_1_3_2_84_2","volume-title":"Proceedings of the KDD","author":"Lin Junyang","year":"2021","unstructured":"Junyang Lin, Rui Men, An Yang, Chang Zhou, Yichang Zhang, Peng Wang, Jingren Zhou, Jie Tang, and Hongxia Yang. 2021. M6: Multi-modality-to-multi-modality multitask mega-transformer for unified pretraining. In Proceedings of the KDD."},{"key":"e_1_3_2_85_2","volume-title":"Proceedings of the ICCV","author":"Lin Tsung-Yi","year":"2017","unstructured":"Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Doll\u00e1r. 2017. Focal loss for dense object detection. In Proceedings of the ICCV."},{"key":"e_1_3_2_86_2","unstructured":"Yuanguo Lin Yong Liu Fan Lin Lixin Zou Pengcheng Wu Wenhua Zeng Huanhuan Chen and Chunyan Miao. 2021. A survey on reinforcement learning for recommender systems. arXiv preprint arXiv:2109.10665 (2021)."},{"key":"e_1_3_2_87_2","first-page":"4249","volume-title":"Proceedings of the AAAI","author":"Liu Chang","year":"2021","unstructured":"Chang Liu, Xiaoguang Li, Guohao Cai, Zhenhua Dong, Hong Zhu, and Lifeng Shang. 2021. Noninvasive self-attention for side information fusion in sequential recommendation. In Proceedings of the AAAI. 4249\u20134256."},{"key":"e_1_3_2_88_2","volume-title":"Proceedings of the RecSys","author":"Liu Dugang","year":"2021","unstructured":"Dugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming. 2021. Mitigating confounding bias in recommendation via information bottleneck. In Proceedings of the RecSys."},{"key":"e_1_3_2_89_2","article-title":"Is chatgpt a good recommender? A preliminary study","author":"Liu Junling","year":"2023","unstructured":"Junling Liu, Chao Liu, Renjie Lv, Kang Zhou, and Yan Zhang. 2023. Is chatgpt a good recommender? A preliminary study. ArXiv preprint (2023).","journal-title":"ArXiv preprint"},{"key":"e_1_3_2_90_2","volume-title":"Proceedings of the KDD","author":"Liu Qiao","year":"2018","unstructured":"Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-term attention\/memory priority model for session-based recommendation. In Proceedings of the KDD."},{"key":"e_1_3_2_91_2","first-page":"509","volume-title":"Proceedings of the 14th ACM Conference on Recommender Systems","author":"Liu Siyi","year":"2020","unstructured":"Siyi Liu and Yujia Zheng. 2020. Long-tail session-based recommendation. In Proceedings of the 14th ACM Conference on Recommender Systems. 509\u2013514."},{"key":"e_1_3_2_92_2","volume-title":"Proceedings of the WWW","author":"Liu Yuanxing","year":"2020","unstructured":"Yuanxing Liu, Zhaochun Ren, Wei-Nan Zhang, Wanxiang Che, Ting Liu, and Dawei Yin. 2020. Keywords generation improves e-commerce session-based recommendation. In Proceedings of the WWW."},{"key":"e_1_3_2_93_2","article-title":"Evaluation of session-based recommendation algorithms","author":"Ludewig Malte","year":"2018","unstructured":"Malte Ludewig and Dietmar Jannach. 2018. Evaluation of session-based recommendation algorithms. User Modeling and User-Adapted Interaction 28 (2018), 331\u2013390.","journal-title":"User Modeling and User-Adapted Interaction"},{"key":"e_1_3_2_94_2","volume-title":"Proceedings of the IJCAI","author":"Luo Anjing","year":"2020","unstructured":"Anjing Luo, Pengpeng Zhao, Yanchi Liu, Fuzhen Zhuang, Deqing Wang, Jiajie Xu, Junhua Fang, and Victor S. Sheng. 2020. Collaborative self-attention network for session-based recommendation.. In Proceedings of the IJCAI."},{"key":"e_1_3_2_95_2","volume-title":"Proceedings of the SIGIR","author":"Meng Wenjing","year":"2020","unstructured":"Wenjing Meng, Deqing Yang, and Yanghua Xiao. 2020. Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_96_2","volume-title":"Proceedings of the International Conference on Electronic Commerce and Web Technologies","author":"Modani Natwar","year":"2002","unstructured":"Natwar Modani, Parul A. Mittal, Amit A. Nanavati, and Biplav Srivastava. 2002. Series of dynamic targeted recommendations. In Proceedings of the International Conference on Electronic Commerce and Web Technologies."},{"key":"e_1_3_2_97_2","volume-title":"Proceedings of the International Conference on Electronic Commerce and Web Technologies","author":"Modani Natwar","year":"2005","unstructured":"Natwar Modani, Yogish Sabharwal, and S. Karthik. 2005. A framework for session based recommendations. In Proceedings of the International Conference on Electronic Commerce and Web Technologies."},{"key":"e_1_3_2_98_2","volume-title":"Proceedings of the SIGIR","author":"Narwariya Jyoti","year":"2023","unstructured":"Jyoti Narwariya, Priyanka Gupta, Garima Gupta, Lovekesh Vig, and Gautam Shroff. 2023. X4SR: Post-Hoc explanations for session-based recommendations. In Proceedings of the SIGIR."},{"key":"e_1_3_2_99_2","first-page":"2174","volume-title":"Proceedings of the SIGIR","author":"Ouyang Kai","year":"2023","unstructured":"Kai Ouyang, Xianghong Xu, Miaoxin Chen, Zuotong Xie, Hai-Tao Zheng, Shuangyong Song, and Yu Zhao. 2023. Mining interest trends and adaptively assigning sample weight for session-based recommendation. In Proceedings of the SIGIR. 2174\u20132178."},{"key":"e_1_3_2_100_2","volume-title":"Proceedings of the EMNLP Findings","author":"Ouyang Kai","year":"2022","unstructured":"Kai Ouyang, Xianghong Xu, Chen Tang, Wang Chen, and Haitao Zheng. 2022. Social-aware sparse attention network for session-based social recommendation. In Proceedings of the EMNLP Findings."},{"key":"e_1_3_2_101_2","volume-title":"Proceedings of the CIKM","author":"Pan Zhiqiang","year":"2020","unstructured":"Zhiqiang Pan, Fei Cai, Wanyu Chen, Honghui Chen, and Maarten de Rijke. 2020. Star graph neural networks for session-based recommendation. In Proceedings of the CIKM."},{"key":"e_1_3_2_102_2","volume-title":"Proceedings of the SIGIR","author":"Pan Zhiqiang","year":"2020","unstructured":"Zhiqiang Pan, Fei Cai, Yanxiang Ling, and Maarten de Rijke. 2020. An intent-guided collaborative machine for session-based recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_103_2","volume-title":"Proceedings of the WSDM","author":"Pang Yitong","year":"2022","unstructured":"Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long, and Jian Pei. 2022. Heterogeneous global graph neural networks for personalized session-based recommendation. In Proceedings of the WSDM."},{"key":"e_1_3_2_104_2","volume-title":"Proceedings of the RecSys","author":"Peintner Andreas","year":"2023","unstructured":"Andreas Peintner, Amir Reza Mohammadi, and Eva Zangerle. 2023. SPARE: Shortest path global item relations for efficient session-based recommendation. In Proceedings of the RecSys."},{"key":"e_1_3_2_105_2","volume-title":"Proceedings of the KDD","author":"Perozzi Bryan","year":"2014","unstructured":"Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the KDD."},{"key":"e_1_3_2_106_2","volume-title":"Proceedings of the AAAI","author":"Potter Michael","year":"2022","unstructured":"Michael Potter, Hamlin Liu, Yash Lala, Christian Loanzon, and Yizhou Sun. 2022. GRU4RecBE: A hybrid session-based movie recommendation system (student abstract). In Proceedings of the AAAI."},{"key":"e_1_3_2_107_2","first-page":"2075","volume-title":"Proceedings of the CIKM","author":"Qiao Shutong","year":"2023","unstructured":"Shutong Qiao, Wei Zhou, Junhao Wen, Hongyu Zhang, and Min Gao. 2023. Bi-channel multiple sparse graph attention networks for session-based recommendation. In Proceedings of the CIKM. 2075\u20132084."},{"key":"e_1_3_2_108_2","volume-title":"Proceedings of the CIKM","author":"Qiu Ruihong","year":"2019","unstructured":"Ruihong Qiu, Jingjing Li, Zi Huang, and Hongzhi Yin. 2019. Rethinking the item order in session-based recommendation with graph neural networks. In Proceedings of the CIKM."},{"key":"e_1_3_2_109_2","volume-title":"Proceedings of the SIGIR","author":"Qiu Ruihong","year":"2020","unstructured":"Ruihong Qiu, Hongzhi Yin, Zi Huang, and Tong Chen. 2020. Gag: Global attributed graph neural network for streaming session-based recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_110_2","volume-title":"Proceedings of the RecSys","author":"Quadrana Massimo","year":"2017","unstructured":"Massimo Quadrana, Alexandros Karatzoglou, Bal\u00e1zs Hidasi, and Paolo Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the RecSys."},{"key":"e_1_3_2_111_2","volume-title":"Proceedings of the WSDM","author":"Kermany Naime Ranjbar","year":"2022","unstructured":"Naime Ranjbar Kermany, Jian Yang, Jia Wu, and Luiz Pizzato. 2022. Fair-srs: A fair session-based recommendation system. In Proceedings of the WSDM."},{"key":"e_1_3_2_112_2","volume-title":"Proceedings of the AAAI","author":"Ren Pengjie","year":"2019","unstructured":"Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten De Rijke. 2019. Repeatnet: A repeat aware neural recommendation machine for session-based recommendation. In Proceedings of the AAAI."},{"key":"e_1_3_2_113_2","first-page":"452","volume-title":"Proceedings of the UAI","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the UAI. 452\u2013461."},{"key":"e_1_3_2_114_2","volume-title":"Proceedings of the WWW","author":"Rendle Steffen","year":"2010","unstructured":"Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the WWW."},{"key":"e_1_3_2_115_2","doi-asserted-by":"crossref","unstructured":"Yossi Rubner Carlo Tomasi and Leonidas J. Guibas. 2000. The earth mover\u2019s distance as a metric for image retrieval. International Journal of Computer Vision 40 2 (2000) 99\u2013121.","DOI":"10.1023\/A:1026543900054"},{"key":"e_1_3_2_116_2","unstructured":"Sebastian Ruder. 2017. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)."},{"key":"e_1_3_2_117_2","article-title":"Dynamic routing between capsules","author":"Sabour Sara","year":"2017","unstructured":"Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. 2017. Dynamic routing between capsules. Proceedings of the NeurIPS (2017).","journal-title":"Proceedings of the NeurIPS"},{"key":"e_1_3_2_118_2","first-page":"2639","volume-title":"Proceedings of the SIGIR","author":"Seol Jinseok Jamie","year":"2022","unstructured":"Jinseok Jamie Seol, Youngrok Ko, and Sang-goo Lee. 2022. Exploiting session information in BERT-based session-aware sequential recommendation. In Proceedings of the SIGIR. 2639\u20132644."},{"key":"e_1_3_2_119_2","volume-title":"Proceedings of the RecSys","author":"Shalaby Walid","year":"2022","unstructured":"Walid Shalaby, Sejoon Oh, Amir Afsharinejad, Srijan Kumar, and Xiquan Cui. 2022. M2TRec: Metadata-aware multi-task transformer for large-scale and cold-start free session-based recommendations. In Proceedings of the RecSys."},{"key":"e_1_3_2_120_2","unstructured":"Guy Shani David Heckerman Ronen I. Brafman and Craig Boutilier. 2005. An MDP-based recommender system.Journal of Machine Learning Research 6 9 (2005)."},{"key":"e_1_3_2_121_2","doi-asserted-by":"crossref","unstructured":"Bo Shao Dingding Wang Tao Li and Mitsunori Ogihara. 2009. Music recommendation based on acoustic features and user access patterns. IEEE Transactions on Audio Speech and Language Processing 17 8 (2009) 1602\u20131611.","DOI":"10.1109\/TASL.2009.2020893"},{"key":"e_1_3_2_122_2","first-page":"464","volume-title":"Proceedings of the NAACL","author":"Shaw Peter","year":"2018","unstructured":"Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018. Self-attention with relative position representations. In Proceedings of the NAACL. 464\u2013468."},{"key":"e_1_3_2_123_2","unstructured":"Qi Shen Lingfei Wu Yitong Pang Yiming Zhang Zhihua Wei Fangli Xu and Bo Long. 2021. Multi-behavior graph contextual aware network for session-based recommendation. arXiv preprint arXiv:2109.11903 (2021)."},{"key":"e_1_3_2_124_2","volume-title":"Proceedings of the ACML","author":"Shen Qi","year":"2023","unstructured":"Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, and Zhihua Wei. 2023. Temporal aware multi-interest graph neural network for session-based recommendation. In Proceedings of the ACML."},{"key":"e_1_3_2_125_2","unstructured":"Shun-Yao Shih and Heng-Yu Chi. 2018. Automatic personalized and flexible playlist generation using reinforcement learning. arXiv preprint arXiv:1809.04214 (2018)."},{"key":"e_1_3_2_126_2","volume-title":"Proceedings of the CIKM","author":"Song Bo","year":"2019","unstructured":"Bo Song, Yi Cao, Weifeng Zhang, and Congfu Xu. 2019. Session-based recommendation with hierarchical memory networks. In Proceedings of the CIKM."},{"key":"e_1_3_2_127_2","volume-title":"Proceedings of the WSDM","author":"Song Weiping","year":"2019","unstructured":"Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. 2019. Session-based social recommendation via dynamic graph attention networks. In Proceedings of the WSDM."},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127063"},{"key":"e_1_3_2_129_2","volume-title":"Proceedings of the WWW","author":"Su Jiajie","year":"2023","unstructured":"Jiajie Su, Chaochao Chen, Weiming Liu, Fei Wu, Xiaolin Zheng, and Haoming Lyu. 2023. Enhancing hierarchy-aware graph networks with deep dual clustering for session-based recommendation. In Proceedings of the WWW."},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","unstructured":"Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Adv. in Artif. Intell. 2009 Article 4 (Jan. 2009) 1 pages. 10.1155\/2009\/421425","DOI":"10.1155\/2009\/421425"},{"key":"e_1_3_2_131_2","unstructured":"Chi Sun Hang Yan Xipeng Qiu and Xuanjing Huang. 2018. Gaussian word embedding with a wasserstein distance loss. arXiv preprint arXiv:1808.07016 (2018)."},{"key":"e_1_3_2_132_2","volume-title":"Proceedings of the CIKM","author":"Sun Fei","year":"2019","unstructured":"Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the CIKM."},{"key":"e_1_3_2_133_2","volume-title":"Proceedings of the 1st Workshop on Deep Learning for Recommender Systems","author":"Tan Yong Kiam","year":"2016","unstructured":"Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems."},{"key":"e_1_3_2_134_2","first-page":"1632","volume-title":"Proceedings of the SIGIR","author":"Tian Yu","year":"2022","unstructured":"Yu Tian, Jianxin Chang, Yanan Niu, Yang Song, and Chenliang Li. 2022. When multi-level meets multi-interest: A multi-grained neural model for sequential recommendation. In Proceedings of the SIGIR. 1632\u20131641."},{"key":"e_1_3_2_135_2","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux Timoth\u00e9e Lacroix Baptiste Rozi\u00e8re Naman Goyal Eric Hambro Faisal Azhar et\u00a0al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"e_1_3_2_136_2","first-page":"5253","volume-title":"Proceedings of the CIKM","author":"Turgut Hacer","year":"2023","unstructured":"Hacer Turgut, Tan Doruk Yetki, \u00d6m\u00fcr Bali, and Tayfun Arda Y\u00fccel. 2023. Prod2Vec-Var: A session based recommendation system with enhanced diversity. In Proceedings of the CIKM. 5253\u20135254."},{"key":"e_1_3_2_137_2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1145\/2043932.2043955","volume-title":"Proceedings of the 5th ACM Conference on Recommender Systems","author":"Vargas Sa\u00fal","year":"2011","unstructured":"Sa\u00fal Vargas and Pablo Castells. 2011. Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the 5th ACM Conference on Recommender Systems. 109\u2013116."},{"key":"e_1_3_2_138_2","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 31st International Conference on Neural Information Processing Systems (Long Beach California USA) (NIPS\u201917). Curran Associates Inc. Red Hook NY USA 6000\u20136010."},{"key":"e_1_3_2_139_2","article-title":"Graph attention networks","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. stat (2017).","journal-title":"stat"},{"key":"e_1_3_2_140_2","unstructured":"Luke Vilnis and Andrew McCallum. 2014. Word representations via gaussian embedding. arXiv preprint arXiv:1412.6623 (2014)."},{"key":"e_1_3_2_141_2","volume-title":"Proceedings of the SDM","author":"Wang Jianling","year":"2021","unstructured":"Jianling Wang, Kaize Ding, Ziwei Zhu, and James Caverlee. 2021. Session-based recommendation with hypergraph attention networks. In Proceedings of the SDM."},{"key":"e_1_3_2_142_2","unstructured":"Lei Wang and Ee-Peng Lim. 2023. Zero-shot next-item recommendation using large pretrained language models. arXiv preprint arXiv:2304.03153 (2023)."},{"key":"e_1_3_2_143_2","volume-title":"Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE\u201922)","author":"Wang Liuyin","year":"2022","unstructured":"Liuyin Wang, Xianghong Xu, Kai Ouyang, Huanzhong Duan, Yanxiong Lu, and Hai-Tao Zheng. 2022. Self-supervised dual-channel attentive network for session-based social recommendation. In Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE\u201922)."},{"key":"e_1_3_2_144_2","volume-title":"Proceedings of the SIGIR","author":"Wang Meirui","year":"2019","unstructured":"Meirui Wang, Pengjie Ren, Lei Mei, Zhumin Chen, Jun Ma, and Maarten de Rijke. 2019. A collaborative session-based recommendation approach with parallel memory modules. In Proceedings of the SIGIR."},{"key":"e_1_3_2_145_2","volume-title":"Proceedings of the SIGIR","author":"Wang Pengfei","year":"2020","unstructured":"Pengfei Wang, Yu Fan, Long Xia, Wayne Xin Zhao, ShaoZhang Niu, and Jimmy Huang. 2020. KERL: A knowledge-guided reinforcement learning model for sequential recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_146_2","article-title":"A survey on session-based recommender systems","author":"Wang Shoujin","year":"2021","unstructured":"Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, and Defu Lian. 2021. A survey on session-based recommender systems. ACM Computing Surveys 54, 7 (2021), 1\u201338.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_2_147_2","volume-title":"Proceedings of the ECML","author":"Wang Shoujin","year":"2017","unstructured":"Shoujin Wang, Liang Hu, and Longbing Cao. 2017. Perceiving the next choice with comprehensive transaction embeddings for online recommendation. In Proceedings of the ECML."},{"key":"e_1_3_2_148_2","first-page":"3425","volume-title":"Proceedings of the SIGIR","author":"Wang Shoujin","year":"2022","unstructured":"Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, and Charu Aggarwal. 2022. Sequential\/session-based recommendations: Challenges, approaches, applications and opportunities. In Proceedings of the SIGIR. 3425\u20133428."},{"key":"e_1_3_2_149_2","first-page":"9929","volume-title":"Proceedings of the ICML","author":"Wang Tongzhou","year":"2020","unstructured":"Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In Proceedings of the ICML. 9929\u20139939."},{"key":"e_1_3_2_150_2","volume-title":"Proceedings of the KDD","author":"Wang Wenjie","year":"2021","unstructured":"Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, and Tat-Seng Chua. 2021. Deconfounded recommendation for alleviating bias amplification. In Proceedings of the KDD."},{"key":"e_1_3_2_151_2","first-page":"832","volume-title":"Proceedings of the SIGIR","author":"Wang Wenjie","year":"2023","unstructured":"Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, and Tat-Seng Chua. 2023. Diffusion recommender model. In Proceedings of the SIGIR. 832\u2013841."},{"key":"e_1_3_2_152_2","volume-title":"Proceedings of the SIGIR","author":"Wang Weiqing","year":"2018","unstructured":"Weiqing Wang, Hongzhi Yin, Zi Huang, Qinyong Wang, Xingzhong Du, and Quoc Viet Hung Nguyen. 2018. Streaming ranking based recommender systems. In Proceedings of the SIGIR."},{"key":"e_1_3_2_153_2","volume-title":"Proceedings of the WWW","author":"Wang Wen","year":"2020","unstructured":"Wen Wang, Wei Zhang, Shukai Liu, Qi Liu, Bo Zhang, Leyu Lin, and Hongyuan Zha. 2020. Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In Proceedings of the WWW."},{"key":"e_1_3_2_154_2","volume-title":"Proceedings of the WWW","author":"Wang Xiao","year":"2019","unstructured":"Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019. Heterogeneous graph attention network. In Proceedings of the WWW."},{"key":"e_1_3_2_155_2","first-page":"779","volume-title":"Proceedings of the WSDM","author":"Wang Yuhao","year":"2024","unstructured":"Yuhao Wang, Ziru Liu, Yichao Wang, Xiangyu Zhao, Bo Chen, Huifeng Guo, and Ruiming Tang. 2024. Diff-MSR: A diffusion model enhanced paradigm for cold-start multi-scenario recommendation. In Proceedings of the WSDM. 779\u2013787."},{"key":"e_1_3_2_156_2","volume-title":"Proceedings of the CIKM","author":"Wang Zhitao","year":"2018","unstructured":"Zhitao Wang, Chengyao Chen, Ke Zhang, Yu Lei, and Wenjie Li. 2018. Variational recurrent model for session-based recommendation. In Proceedings of the CIKM."},{"key":"e_1_3_2_157_2","volume-title":"Proceedings of the SIGIR","author":"Wang Ziyang","year":"2020","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 Proceedings of the SIGIR."},{"key":"e_1_3_2_158_2","volume-title":"Proceedings of the WWW","author":"Wei Chunyu","year":"2022","unstructured":"Chunyu Wei, Bing Bai, Kun Bai, and Fei Wang. 2022. Gsl4rec: Session-based recommendations with collective graph structure learning and next interaction prediction. In Proceedings of the WWW."},{"key":"e_1_3_2_159_2","volume-title":"Proceedings of the KDD","author":"Wei Tianxin","year":"2021","unstructured":"Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the KDD."},{"key":"e_1_3_2_160_2","volume-title":"Proceedings of the RecSys","author":"Wilm Timo","year":"2023","unstructured":"Timo Wilm, Philipp Normann, Sophie Baumeister, and Paul-Vincent Kobow. 2023. Scaling session-based transformer recommendations using optimized negative sampling and loss functions. In Proceedings of the RecSys."},{"key":"e_1_3_2_161_2","volume-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","author":"Wu Chen","year":"2017","unstructured":"Chen Wu and Ming Yan. 2017. Session-aware information embedding for e-commerce product recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management."},{"key":"e_1_3_2_162_2","first-page":"1","article-title":"Causality and correlation graph modeling for effective and explainable session-based recommendation","author":"Wu Huizi","year":"2023","unstructured":"Huizi Wu, Cong Geng, and Hui Fang. 2023. Causality and correlation graph modeling for effective and explainable session-based recommendation. ACM Transactions on the Web 18, 1 (2023), 1\u201325.","journal-title":"ACM Transactions on the Web"},{"key":"e_1_3_2_163_2","doi-asserted-by":"crossref","unstructured":"Shiwen Wu Fei Sun Wentao Zhang Xu Xie and Bin Cui. 2022. Graph neural networks in recommender systems: A survey. Comput. Surveys 55 5 (2022) 1\u201337.","DOI":"10.1145\/3535101"},{"key":"e_1_3_2_164_2","volume-title":"Proceedings of the AAAI","author":"Wu Shu","year":"2019","unstructured":"Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI."},{"key":"e_1_3_2_165_2","volume-title":"Proceedings of the KDD","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020. Connecting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the KDD."},{"key":"e_1_3_2_166_2","volume-title":"Proceedings of the CIKM","author":"Xia Xin","year":"2021","unstructured":"Xin Xia, Hongzhi Yin, Junliang Yu, Yingxia Shao, and Lizhen Cui. 2021. Self-supervised graph co-training for session-based recommendation. In Proceedings of the CIKM."},{"key":"e_1_3_2_167_2","volume-title":"Proceedings of the AAAI","author":"Xia Xin","year":"2021","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 Proceedings of the AAAI."},{"key":"e_1_3_2_168_2","article-title":"Efficient on-device session-based recommendation","author":"Xia Xin","year":"2023","unstructured":"Xin Xia, Junliang Yu, Qinyong Wang, Chaoqun Yang, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2023. Efficient on-device session-based recommendation. ACM Transactions on Information Systems 41, 4 (2023), 1\u201324.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_169_2","first-page":"2795","volume-title":"Proceedings of the CIKM","author":"Xia Xin","year":"2023","unstructured":"Xin Xia, Junliang Yu, Guandong Xu, and Hongzhi Yin. 2023. Towards communication-efficient model updating for on-device session-based recommendation. In Proceedings of the CIKM. 2795\u20132804."},{"key":"e_1_3_2_170_2","first-page":"285","volume-title":"Proceedings of the SIGIR","author":"Xian Yikun","year":"2019","unstructured":"Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, and Yongfeng Zhang. 2019. Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of the SIGIR. 285\u2013294."},{"key":"e_1_3_2_171_2","first-page":"1611","volume-title":"Proceedings of the SIGIR","author":"Xie Yueqi","year":"2022","unstructured":"Yueqi Xie, Peilin Zhou, and Sunghun Kim. 2022. Decoupled side information fusion for sequential recommendation. In Proceedings of the SIGIR. 1611\u20131621."},{"key":"e_1_3_2_172_2","first-page":"855","volume-title":"Proceedings of the WSDM","author":"Xin Xin","year":"2024","unstructured":"Xin Xin, Liu Yang, Ziqi Zhao, Pengjie Ren, Zhumin Chen, Jun Ma, and Zhaochun Ren. 2024. On the effectiveness of unlearning in session-based recommendation. In Proceedings of the WSDM. 855\u2013863."},{"key":"e_1_3_2_173_2","first-page":"1","article-title":"Deconfounded causal collaborative filtering","author":"Xu Shuyuan","year":"2023","unstructured":"Shuyuan Xu, Juntao Tan, Shelby Heinecke, Vena Jia Li, and Yongfeng Zhang. 2023. Deconfounded causal collaborative filtering. ACM Transactions on Recommender Systems 1, 4 (2023), 1\u201325.","journal-title":"ACM Transactions on Recommender Systems"},{"key":"e_1_3_2_174_2","volume-title":"Proceedings of the CIKM","author":"Xu Xianghong","year":"2022","unstructured":"Xianghong Xu, Kai Ouyang, Liuyin Wang, Jiaxin Zou, Yanxiong Lu, Hai-Tao Zheng, and Hong-Gee Kim. 2022. Modeling latent autocorrelation for session-based recommendation. In Proceedings of the CIKM."},{"key":"e_1_3_2_175_2","first-page":"1029","volume-title":"Proceedings of the WSDM","author":"Yang Aobo","year":"2021","unstructured":"Aobo Yang, Nan Wang, Hongbo Deng, and Hongning Wang. 2021. Explanation as a defense of recommendation. In Proceedings of the WSDM. 1029\u20131037."},{"key":"e_1_3_2_176_2","doi-asserted-by":"crossref","unstructured":"Ling Yang Zhilong Zhang Yang Song Shenda Hong Runsheng Xu Yue Zhao Wentao Zhang Bin Cui and Ming-Hsuan Yang. 2023. Diffusion models: A comprehensive survey of methods and applications. Comput. Surveys 56 4 (2023) 1\u201339.","DOI":"10.1145\/3626235"},{"key":"e_1_3_2_177_2","volume-title":"Proceedings of the CIKM","author":"Yang Mengyue","year":"2021","unstructured":"Mengyue Yang, Quanyu Dai, Zhenhua Dong, Xu Chen, Xiuqiang He, and Jun Wang. 2021. Top-N recommendation with counterfactual user preference simulation. In Proceedings of the CIKM."},{"key":"e_1_3_2_178_2","volume-title":"Proceedings of the RecSys","author":"Yang Yaming","year":"2023","unstructured":"Yaming Yang, Jieyu Zhang, Yujing Wang, Zheng Miao, and Yunhai Tong. 2023. Multiple connectivity views for session-based recommendation. In Proceedings of the RecSys."},{"key":"e_1_3_2_179_2","volume-title":"Proceedings of the DASFAA","author":"Yap Ghim-Eng","year":"2012","unstructured":"Ghim-Eng Yap, Xiao-Li Li, and Philip S. Yu. 2012. Effective next-items recommendation via personalized sequential pattern mining. In Proceedings of the DASFAA."},{"key":"e_1_3_2_180_2","volume-title":"Proceedings of the ICDM","author":"Ye Rui","year":"2020","unstructured":"Rui Ye, Qing Zhang, and Hengliang Luo. 2020. Cross-session aware temporal convolutional network for session-based recommendation. In Proceedings of the ICDM."},{"key":"e_1_3_2_181_2","first-page":"1","article-title":"Understanding diversity in session-based recommendation","author":"Yin Qing","year":"2023","unstructured":"Qing Yin, Hui Fang, Zhu Sun, and Yew-Soon Ong. 2023. Understanding diversity in session-based recommendation. ACM Transactions on Information Systems 42, 1 (2023), 1\u201334.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_182_2","volume-title":"Proceedings of the KDD","author":"Ying Rex","year":"2018","unstructured":"Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the KDD."},{"key":"e_1_3_2_183_2","first-page":"3083","volume-title":"Proceedings of the CIKM","author":"Yu Dianer","year":"2023","unstructured":"Dianer Yu, Qian Li, Hongzhi Yin, and Guandong Xu. 2023. Causality-guided graph learning for session-based recommendation. In Proceedings of the CIKM. 3083\u20133093."},{"key":"e_1_3_2_184_2","volume-title":"Proceedings of the SIGIR","author":"Yu Feng","year":"2020","unstructured":"Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2020. TAGNN: Target attentive graph neural networks for session-based recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_185_2","volume-title":"Proceedings of the WWW","author":"Yuan Fajie","year":"2020","unstructured":"Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, and Yilin Xiong. 2020. Future data helps training: Modeling future contexts for session-based recommendation. In Proceedings of the WWW."},{"key":"e_1_3_2_186_2","volume-title":"Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE\u201922)","author":"Yuan Jiahao","year":"2022","unstructured":"Jiahao Yuan, Wendi Ji, Dell Zhang, Jinwei Pan, and Xiaoling Wang. 2022. Micro-behavior encoding for session-based recommendation. In Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE\u201922)."},{"key":"e_1_3_2_187_2","volume-title":"Proceedings of the AAAI","author":"Yuan Jiahao","year":"2021","unstructured":"Jiahao Yuan, Zihan Song, Mingyou Sun, Xiaoling Wang, and Wayne Xin Zhao. 2021. Dual sparse attention network for session-based recommendation. In Proceedings of the AAAI."},{"key":"e_1_3_2_188_2","volume-title":"Proceedings of the 1st International Workshop on Internet-scale Multimedia Management","author":"Zangerle Eva","year":"2014","unstructured":"Eva Zangerle, Martin Pichl, Wolfgang Gassler, and G\u00fcnther Specht. 2014. # nowplaying music dataset: Extracting listening behavior from Twitter. In Proceedings of the 1st International Workshop on Internet-scale Multimedia Management."},{"key":"e_1_3_2_189_2","unstructured":"Huimin Zeng Zhenrui Yue Qian Jiang and Dong Wang. 2024. Federated recommendation via hybrid retrieval augmented generation. arXiv preprint arXiv:2403.04256 (2024)."},{"key":"e_1_3_2_190_2","article-title":"Personalized graph neural networks with attention mechanism for session-aware recommendation","author":"Zhang Mengqi","year":"2020","unstructured":"Mengqi Zhang, Shu Wu, Meng Gao, Xin Jiang, Ke Xu, and Liang Wang. 2020. Personalized graph neural networks with attention mechanism for session-aware recommendation. IEEE Transactions on Knowledge and Data Engineering 34, 8 (2020), 3946\u20133957.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_191_2","volume-title":"Proceedings of the WSDM","author":"Zhang Peiyan","year":"2023","unstructured":"Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jae Boum Kim, Yan Zhang, Xing Xie, Haohan Wang, and Sunghun Kim. 2023. Efficiently leveraging multi-level user intent for session-based recommendation via atten-mixer network. In Proceedings of the WSDM."},{"key":"e_1_3_2_192_2","unstructured":"Rongzhi Zhang Yulong Gu Xiaoyu Shen and Hui Su. 2021. Knowledge-enhanced session-based recommendation with temporal transformer. arXiv preprint arXiv:2112.08745 (2021)."},{"key":"e_1_3_2_193_2","first-page":"1684","volume-title":"Proceedings of the SIGIR","author":"Zhang Xiaokun","year":"2022","unstructured":"Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu, and Hongfei Lin. 2022. Price does matter! Modeling price and interest preferences in session-based recommendation. In Proceedings of the SIGIR. 1684\u20131693."},{"key":"e_1_3_2_194_2","doi-asserted-by":"publisher","unstructured":"Yongfeng Zhang and Xu Chen. 2020. Explainable recommendation: A survey and new perspectives. Found. Trends Inf. Retr. 14 1 (March 2020) 1\u2013101. 10.1561\/1500000066","DOI":"10.1561\/1500000066"},{"key":"e_1_3_2_195_2","volume-title":"Proceedings of the SIGIR","author":"Zhang Yang","year":"2021","unstructured":"Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. Causal intervention for leveraging popularity bias in recommendation. In Proceedings of the SIGIR."},{"key":"e_1_3_2_196_2","first-page":"83","volume-title":"Proceedings of the SIGIR","author":"Zhang Yongfeng","year":"2014","unstructured":"Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the SIGIR. 83\u201392."},{"key":"e_1_3_2_197_2","doi-asserted-by":"crossref","DOI":"10.1093\/nsr\/nwx105","article-title":"An overview of multi-task learning","author":"Zhang Yu","year":"2018","unstructured":"Yu Zhang and Qiang Yang. 2018. An overview of multi-task learning. National Science Review 5, 1 (2018), 30\u201343.","journal-title":"National Science Review"},{"key":"e_1_3_2_198_2","volume-title":"Proceedings of the IEEE\/WIC\/ACM International Conference on Web Intelligence (WI\u201907)","author":"Zhang Zhiyong","year":"2007","unstructured":"Zhiyong Zhang and Olfa Nasraoui. 2007. Efficient hybrid Web recommendations based on Markov clickstream models and implicit search. In Proceedings of the IEEE\/WIC\/ACM International Conference on Web Intelligence (WI\u201907)."},{"key":"e_1_3_2_199_2","volume-title":"Proceedings of the ICDM","author":"Zhang Zizhuo","year":"2021","unstructured":"Zizhuo Zhang and Bang Wang. 2021. Graph neighborhood routing and random walk for session-based recommendation. In Proceedings of the ICDM."},{"key":"e_1_3_2_200_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.neunet.2022.12.003","article-title":"Graph spring network and informative anchor selection for session-based recommendation","author":"Zhang Zizhuo","year":"2023","unstructured":"Zizhuo Zhang and Bang Wang. 2023. Graph spring network and informative anchor selection for session-based recommendation. Neural Networks 159 (2023), 43\u201356.","journal-title":"Neural Networks"},{"key":"e_1_3_2_201_2","volume-title":"Proceedings of the WWW","author":"Zheng Yu","year":"2021","unstructured":"Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin. 2021. Disentangling user interest and conformity for recommendation with causal embedding. In Proceedings of the WWW."},{"key":"e_1_3_2_202_2","volume-title":"Proceedings of the ICDM","author":"Zheng Yujia","year":"2020","unstructured":"Yujia Zheng, Siyi Liu, Zekun Li, and Shu Wu. 2020. Dgtn: Dual-channel graph transition network for session-based recommendation. In Proceedings of the ICDM."},{"key":"e_1_3_2_203_2","volume-title":"Proceedings of the WWW","author":"Zhou Fan","year":"2019","unstructured":"Fan Zhou, Zijing Wen, Kunpeng Zhang, Goce Trajcevski, and Ting Zhong. 2019. Variational session-based recommendation using normalizing flows. In Proceedings of the WWW."},{"key":"e_1_3_2_204_2","volume-title":"Proceedings of the SIGIR","author":"Zhou Huachi","year":"2021","unstructured":"Huachi Zhou, Qiaoyu Tan, Xiao Huang, Kaixiong Zhou, and Xiaoling Wang. 2021. Temporal augmented graph neural networks for session-based recommendations. In Proceedings of the SIGIR."},{"key":"e_1_3_2_205_2","unstructured":"Guanghui Zhu Haojun Hou Jingfan Chen Chunfeng Yuan and Yihua Huang. 2022. Transition relation aware self-attention for session-based recommendation. arXiv preprint arXiv:2203.06407 (2022)."},{"key":"e_1_3_2_206_2","first-page":"1208","volume-title":"Proceedings of the WWW","author":"Zhu Zhihao","year":"2023","unstructured":"Zhihao Zhu, Chenwang Wu, Rui Fan, Defu Lian, and Enhong Chen. 2023. Membership inference attacks against sequential recommender systems. In Proceedings of the WWW. 1208\u20131219."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696413","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696413","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:55Z","timestamp":1750295935000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696413"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"references-count":205,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2,28]]}},"alternative-id":["10.1145\/3696413"],"URL":"https:\/\/doi.org\/10.1145\/3696413","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,7]]},"assertion":[{"value":"2023-10-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}