{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:41:07Z","timestamp":1773888067950,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11704-022-2100-y","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T07:03:09Z","timestamp":1673506989000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation"],"prefix":"10.1007","volume":"17","author":[{"given":"Jinwei","family":"Luo","sequence":"first","affiliation":[]},{"given":"Mingkai","family":"He","sequence":"additional","affiliation":[]},{"given":"Weike","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"2100_CR1","doi-asserted-by":"crossref","unstructured":"Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 263\u2013272","DOI":"10.1109\/ICDM.2008.22"},{"key":"2100_CR2","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T S. Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web. 2017, 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"issue":"7","key":"2100_CR3","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1145\/3465401","volume":"54","author":"S Wang","year":"2022","unstructured":"Wang S, Cao L, Wang Y, Sheng Q Z, Orgun M A, Lian D. A survey on session-based recommender systems. ACM Computing Surveys, 2022, 54(7): 154","journal-title":"ACM Computing Surveys"},{"issue":"1","key":"2100_CR4","first-page":"20","volume":"40","author":"W Chen","year":"2021","unstructured":"Chen W, Ren P, Cai F, Sun F, De Rijke M. Multi-interest diversification for end-to-end sequential recommendation. ACM Transactions on Information System, 2021, 40(1): 20","journal-title":"ACM Transactions on Information System"},{"key":"2100_CR5","doi-asserted-by":"crossref","unstructured":"Meng W, Yang D, Xiao Y. Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 1091\u20131100","DOI":"10.1145\/3397271.3401098"},{"key":"2100_CR6","doi-asserted-by":"crossref","unstructured":"Wen W, Zhang W, Liu S, Liu Q, Zhang B, Lin L, Zha H. Beyond clicks: modeling multi-relational item graph for session-based target behavior prediction. In: Proceedings of the Web Conference 2020. 2020, 3056\u20133062","DOI":"10.1145\/3366423.3380077"},{"key":"2100_CR7","doi-asserted-by":"crossref","unstructured":"Wang J, Louca R, Hu D, Cellier C, Caverlee J, Hong L. Time to shop for valentine\u2019s day: shopping occasions and sequential recommendation in E-commerce. In: Proceedings of the 13th International Conference on Web Search and Data Mining. 2020, 645\u2013653","DOI":"10.1145\/3336191.3371836"},{"issue":"6","key":"2100_CR8","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/TKDE.2017.2661760","volume":"29","author":"Q Liu","year":"2017","unstructured":"Liu Q, Wu S, Wang L. Multi-behavioral sequential prediction with recurrent log-bilinear model. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(6): 1254\u20131267","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"2100_CR9","doi-asserted-by":"crossref","unstructured":"Li Z, Zhao H, Liu Q, Huang Z, Mei T, Chen E. Learning from history and present: next-item recommendation via discriminatively exploiting user behaviors. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018, 1734\u20131743","DOI":"10.1145\/3219819.3220014"},{"key":"2100_CR10","doi-asserted-by":"crossref","unstructured":"Zhou M, Ding Z, Tang J, Yin D. Micro behaviors: a new perspective in E-commerce recommender systems. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018, 727\u2013735","DOI":"10.1145\/3159652.3159671"},{"key":"2100_CR11","doi-asserted-by":"crossref","unstructured":"Gu Y, Ding Z, Wang S, Zou L, Liu Y, Yin D. Deep multifaceted transformers for multi-objective ranking in large-scale E-commerce recommender systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020, 2493\u20132500","DOI":"10.1145\/3340531.3412697"},{"key":"2100_CR12","doi-asserted-by":"crossref","unstructured":"Xie R, Ling C, Wang Y, Wang R, Xia F, Lin L. Deep feedback network for recommendation. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2020, 2519\u20132525","DOI":"10.24963\/ijcai.2020\/349"},{"key":"2100_CR13","doi-asserted-by":"crossref","unstructured":"Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. 2001, 285\u2013295","DOI":"10.1145\/371920.372071"},{"key":"2100_CR14","doi-asserted-by":"crossref","unstructured":"Jannach D, Ludewig M. When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the 11th ACM Conference on Recommender Systems. 2017, 306\u2013310","DOI":"10.1145\/3109859.3109872"},{"key":"2100_CR15","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2009, 452\u2013461"},{"key":"2100_CR16","doi-asserted-by":"crossref","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web. 2010, 811\u2013820","DOI":"10.1145\/1772690.1772773"},{"key":"2100_CR17","doi-asserted-by":"crossref","unstructured":"He R, McAuley J. Fusing similarity models with Markov chains for sparse sequential recommendation. In: Proceedings of the 16th IEEE International Conference on Data Mining. 2016, 191\u2013200","DOI":"10.1109\/ICDM.2016.0030"},{"key":"2100_CR18","doi-asserted-by":"crossref","unstructured":"Donkers T, Loepp B, Ziegler J. Sequential user-based recurrent neural network recommendations. In: Proceedings of the 11th ACM Conference on Recommender Systems. 2017, 152\u2013160","DOI":"10.1145\/3109859.3109877"},{"key":"2100_CR19","doi-asserted-by":"crossref","unstructured":"Quadrana M, Karatzoglou A, Hidasi B, Cremonesi P. Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the 11th ACM Conference on Recommender Systems. 2017, 130\u2013137","DOI":"10.1145\/3109859.3109896"},{"key":"2100_CR20","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D. Session-based recommendations with recurrent neural networks. In: Proceedings of the 4th International Conference on Learning Representations. 2016"},{"key":"2100_CR21","doi-asserted-by":"crossref","unstructured":"Yu F, Liu Q, Wu S, Wang L, Tan T. A dynamic recurrent model for next basket recommendation. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2016, 729\u2013732","DOI":"10.1145\/2911451.2914683"},{"key":"2100_CR22","doi-asserted-by":"crossref","unstructured":"Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J. Neural attentive session-based recommendation. In: Proceedings of 2017 ACM on Conference on Information and Knowledge Management. 2017, 1419\u20131428","DOI":"10.1145\/3132847.3132926"},{"key":"2100_CR23","doi-asserted-by":"crossref","unstructured":"Liu Q, Zeng Y, Mokhosi R, Zhang H. STAMP: short-term attention\/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018, 1831\u20131839","DOI":"10.1145\/3219819.3219950"},{"key":"2100_CR24","doi-asserted-by":"crossref","unstructured":"Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X, Yan Y, Jin J Li H, Gai K. Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018, 1059\u20131068","DOI":"10.1145\/3219819.3219823"},{"key":"2100_CR25","doi-asserted-by":"crossref","unstructured":"Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T. Session-based recommendation with graph neural networks. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019, 346\u2013353","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"2100_CR26","doi-asserted-by":"crossref","unstructured":"Xu C, Zhao P, Liu Y, Sheng V S, Xu J, Zhuang F, Fang J, Zhou X. Graph contextualized self-attention network for session-based recommendation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 3940\u20133946","DOI":"10.24963\/ijcai.2019\/547"},{"key":"2100_CR27","doi-asserted-by":"crossref","unstructured":"Qiu R, Li J, Huang Z, Yin H. Rethinking the item order in session-based recommendation with graph neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019, 579\u2013588","DOI":"10.1145\/3357384.3358010"},{"key":"2100_CR28","doi-asserted-by":"crossref","unstructured":"Xia X, Yin H, Yu J, Wang Q, Cui L, Zhang X. Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, 4503\u20134518","DOI":"10.1609\/aaai.v35i5.16578"},{"key":"2100_CR29","doi-asserted-by":"crossref","unstructured":"Wang J, Ding K, Zhu Z, Caverlee J. Session-based recommendation with hypergraph attention networks. In: Proceedings of 2021 SIAM International Conference on Data Mining. 2021, 82\u201390","DOI":"10.1137\/1.9781611976700.10"},{"key":"2100_CR30","doi-asserted-by":"crossref","unstructured":"Huang C, Chen J, Xia L, Xu Y, Dai P, Chen Y, Bo L, Zhao J, Huang J X. Graph-enhanced multi-task learning of multi-level transition dynamics for session-based recommendation. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, 4123\u20134130","DOI":"10.1609\/aaai.v35i5.16534"},{"key":"2100_CR31","doi-asserted-by":"crossref","unstructured":"Wang Z, Wei W, Cong G, Li X L, Mao X L, Qiu M. Global context enhanced graph neural networks for session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 169\u2013178","DOI":"10.1145\/3397271.3401142"},{"key":"2100_CR32","doi-asserted-by":"crossref","unstructured":"Tang J, Wang K. Personalized top-N sequential recommendation via convolutional sequence embedding. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018, 565\u2013573","DOI":"10.1145\/3159652.3159656"},{"key":"2100_CR33","doi-asserted-by":"crossref","unstructured":"Yuan F, Karatzoglou A, Arapakis I, Jose J M, He X. A simple convolutional generative network for next item recommendation. In: Proceedings of the 12th ACM International Conference on Web Search and Data Mining. 2019, 582\u2013590","DOI":"10.1145\/3289600.3290975"},{"key":"2100_CR34","doi-asserted-by":"crossref","unstructured":"Wang C K, McAuley J. Self-attentive sequential recommendation. In: Proceedings of 2018 IEEE International Conference on Data Mining. 2018, 197\u2013206","DOI":"10.1109\/ICDM.2018.00035"},{"key":"2100_CR35","doi-asserted-by":"crossref","unstructured":"Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P. BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019, 1441\u20131450","DOI":"10.1145\/3357384.3357895"},{"key":"2100_CR36","doi-asserted-by":"crossref","unstructured":"Zhou K, Wang H, Zhao W X, Zhu Y, Wang S, Zhang F, Wang Z, Wen J R. S3-Rec: self-supervised learning for sequential recommendation with mutual information maximization. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020, 1893\u20131902","DOI":"10.1145\/3340531.3411954"},{"key":"2100_CR37","doi-asserted-by":"crossref","unstructured":"Ma C, Ma L, Zhang Y, Sun J, Liu X, Coates M. Memory augmented graph neural networks for sequential recommendation. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 5045\u20135052","DOI":"10.1609\/aaai.v34i04.5945"},{"key":"2100_CR38","doi-asserted-by":"crossref","unstructured":"Chang J, Gao C, Zheng Y, Hui Y, Niu Y, Song Y, Jin D, Li Y. Sequential recommendation with graph neural networks. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021, 378\u2013387","DOI":"10.1145\/3404835.3462968"},{"issue":"4","key":"2100_CR39","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1145\/3402521","volume":"38","author":"X Chen","year":"2020","unstructured":"Chen X, Li L, Pan W, Ming Z. A survey on heterogeneous one-class collaborative filtering. ACM Transactions on Information Systems, 2020, 38(4): 35","journal-title":"ACM Transactions on Information Systems"},{"key":"2100_CR40","doi-asserted-by":"crossref","unstructured":"Loni B, Pagano R, Larson M, Hanjalic A. Bayesian personalized ranking with multi-channel user feedback. In: Proceedings of the 10th ACM Conference on Recommender Systems. 2016, 361\u2013364","DOI":"10.1145\/2959100.2959163"},{"key":"2100_CR41","doi-asserted-by":"crossref","unstructured":"Gao C, He X, Gan D, Chen X, Feng F, Li Y, Chua T S, Jin D. Neural multi-task recommendation from multi-behavior data. In: Proceedings of the 35th IEEE International Conference on Data Engineering. 2019, 1554\u20131557","DOI":"10.1109\/ICDE.2019.00140"},{"key":"2100_CR42","doi-asserted-by":"crossref","unstructured":"Jin B, Gao C, He X, Jin D, Li Y. Multi-behavior recommendation with graph convolutional networks. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 659\u2013668","DOI":"10.1145\/3397271.3401072"},{"key":"2100_CR43","doi-asserted-by":"crossref","unstructured":"Xia L, Huang C, Xu Y, Dai P, Zhang B, Bo L. Multiplex behavioral relation learning for recommendation via memory augmented transformer network. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 2397\u20132406","DOI":"10.1145\/3397271.3401445"},{"key":"2100_CR44","doi-asserted-by":"crossref","unstructured":"Chen C, Zhang M, Zhang Y, Ma W, Liu Y, Ma S. Efficient heterogeneous collaborative filtering without negative sampling for recommendation. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 19\u201326","DOI":"10.1609\/aaai.v34i01.5329"},{"key":"2100_CR45","doi-asserted-by":"crossref","unstructured":"Xia L, Huang C, Xu Y, Dai P, Zhang X, Yang H, Pei J, Bo L. Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, 4486\u20134493","DOI":"10.1609\/aaai.v35i5.16576"},{"key":"2100_CR46","doi-asserted-by":"crossref","unstructured":"Chen C, Ma W, Zhang M, Wang Z, He X, Wang C, Liu Y, Ma S. Graph heterogeneous multi-relational recommendation. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, 3958\u20133966","DOI":"10.1609\/aaai.v35i5.16515"},{"key":"2100_CR47","doi-asserted-by":"crossref","unstructured":"Guo L, Hua L, Jia R, Zhao B, Wang X, Cui B. Buying or browsing?: predicting real-time purchasing intent using attention-based deep network with multiple behavior. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 1984\u20131992","DOI":"10.1145\/3292500.3330670"},{"key":"2100_CR48","unstructured":"Hamilton W L, Ying R, Leskovec J. Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 1025\u20131035"},{"key":"2100_CR49","unstructured":"Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 2010, 249\u2013256"},{"issue":"86","key":"2100_CR50","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008, 9(86): 2579\u20132605","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"2100_CR51","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1145\/3432244","volume":"39","author":"C Wang","year":"2021","unstructured":"Wang C, Ma W, Zhang M, Chen C, Liu Y, Ma S. Toward dynamic user intention: temporal evolutionary effects of item relations in sequential recommendation. ACM Transactions on Information Systems, 2021, 39(2): 16","journal-title":"ACM Transactions on Information Systems"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-022-2100-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-022-2100-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-022-2100-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T21:30:35Z","timestamp":1731965435000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-022-2100-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,12]]},"references-count":51,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["2100"],"URL":"https:\/\/doi.org\/10.1007\/s11704-022-2100-y","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,12]]},"assertion":[{"value":"18 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"175336"}}