{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:24:32Z","timestamp":1760711072261,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62077027"],"award-info":[{"award-number":["No.62077027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["No.823783"],"award-info":[{"award-number":["No.823783"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s13042-022-01702-8","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T10:03:58Z","timestamp":1667469838000},"page":"1317-1331","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Neighbor-enhanced graph transition network for session-based recommendation"],"prefix":"10.1007","volume":"14","author":[{"given":"Zijing","family":"Yi","sequence":"first","affiliation":[]},{"given":"Rui","family":"Song","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"issue":"3","key":"1702_CR1","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s11257-017-9194-1","volume":"27","author":"D Jannach","year":"2017","unstructured":"Jannach D, Ludewig M, Lerche L (2017) Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Model User-Adap Inter 27(3):351\u2013392. https:\/\/doi.org\/10.1007\/s11257-017-9194-1","journal-title":"User Model User-Adap Inter"},{"issue":"7","key":"1702_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3465401","volume":"54","author":"S Wang","year":"2021","unstructured":"Wang S, Cao L, Wang Y, Sheng QZ, Orgun MA, Lian D (2021) A survey on session-based recommender systems. ACM Comput Surv (CSUR) 54(7):1\u201338. https:\/\/doi.org\/10.1145\/3465401","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1702_CR3","unstructured":"Shani G, Heckerman D, Brafman RI, Boutilier C (2005) An mdp-based recommender system. J Mach Learn Res 6(9). http:\/\/jmlr.org\/papers\/v6\/shani05a.html"},{"key":"1702_CR4","doi-asserted-by":"publisher","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World Wide Web, pp 811\u2013820. https:\/\/doi.org\/10.1145\/1772690.1772773","DOI":"10.1145\/1772690.1772773"},{"key":"1702_CR5","unstructured":"Zimdars A, Chickering DM, Meek C (2013) Using temporal data for making recommendations. arXiv preprint arXiv:1301.2320"},{"key":"1702_CR6","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939"},{"key":"1702_CR7","doi-asserted-by":"publisher","unstructured":"Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J (2017) Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp. 1419\u20131428. https:\/\/doi.org\/10.1145\/3132847.3132926","DOI":"10.1145\/3132847.3132926"},{"key":"1702_CR8","doi-asserted-by":"publisher","unstructured":"Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 346\u2013353. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301346","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"1702_CR9","doi-asserted-by":"publisher","unstructured":"Xu C, Zhao P, Liu Y, Sheng VS, Xu J, Zhuang F, Fang J, Zhou X (2019) Graph contextualized self-attention network for session-based recommendation. In: IJCAI, vol 19, pp 3940\u20133946. https:\/\/doi.org\/10.24963\/ijcai.2019\/547","DOI":"10.24963\/ijcai.2019\/547"},{"key":"1702_CR10","doi-asserted-by":"publisher","unstructured":"Qiu R, Li J, Huang Z, Yin H (2019) 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, pp 579\u2013588. https:\/\/doi.org\/10.1145\/3357384.3358010","DOI":"10.1145\/3357384.3358010"},{"key":"1702_CR11","doi-asserted-by":"publisher","unstructured":"Chen T, Wong RC-W (2020) Handling information loss of graph neural networks for session-based recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1172\u20131180. https:\/\/doi.org\/10.1145\/3394486.3403170","DOI":"10.1145\/3394486.3403170"},{"key":"1702_CR12","doi-asserted-by":"publisher","unstructured":"Pan Z, Cai F, Chen W, Chen H, de Rijke M (2020) Star graph neural networks for session-based recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 1195\u20131204. https:\/\/doi.org\/10.1145\/3340531.3412014","DOI":"10.1145\/3340531.3412014"},{"key":"1702_CR13","doi-asserted-by":"publisher","unstructured":"Wang M, Ren P, Mei L, Chen Z, Ma J, de Rijke M (2019) A collaborative session-based recommendation approach with parallel memory modules. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 345\u2013354. https:\/\/doi.org\/10.1145\/3331184.3331210","DOI":"10.1145\/3331184.3331210"},{"key":"1702_CR14","doi-asserted-by":"publisher","unstructured":"Luo A, Zhao P, Liu Y, Zhuang F, Wang D, Xu J, Fang J, Sheng VS (2020) Collaborative self-attention network for session-based recommendation. In: IJCAI, pp 2591\u20132597. https:\/\/doi.org\/10.24963\/ijcai.2020\/359","DOI":"10.24963\/ijcai.2020\/359"},{"key":"1702_CR15","doi-asserted-by":"publisher","unstructured":"Liu Q, Zeng Y, Mokhosi R, Zhang H (2018) 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, pp 1831\u20131839. https:\/\/doi.org\/10.1145\/3219819.3219950","DOI":"10.1145\/3219819.3219950"},{"key":"1702_CR16","doi-asserted-by":"publisher","unstructured":"Ye R, Zhang Q, Luo H (2020) Cross-session aware temporal convolutional network for session-based recommendation. In: 2020 International conference on data mining workshops (ICDMW). IEEE, pp 220\u2013226. https:\/\/doi.org\/10.1109\/ICDMW51313.2020.00039","DOI":"10.1109\/ICDMW51313.2020.00039"},{"key":"1702_CR17","doi-asserted-by":"publisher","unstructured":"Eirinaki M, Vazirgiannis M, Kapogiannis D (2005) Web path recommendations based on page ranking and markov models. In: Proceedings of the 7th annual ACM international workshop on web information and data management, pp 2\u20139. https:\/\/doi.org\/10.1145\/1097047.1097050","DOI":"10.1145\/1097047.1097050"},{"key":"1702_CR18","doi-asserted-by":"crossref","unstructured":"Tan YK, Xu X, Liu Y (2016) Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st workshop on deep learning for recommender systems, pp 17\u201322. arXiv:1606.08117","DOI":"10.1145\/2988450.2988452"},{"key":"1702_CR19","doi-asserted-by":"publisher","unstructured":"Kang W-C., McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM), pp 197\u2013206. https:\/\/doi.org\/10.1109\/ICDM.2018.00035. IEEE","DOI":"10.1109\/ICDM.2018.00035"},{"key":"1702_CR20","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp. 5998\u20136008. arXiv:1706.03762"},{"key":"1702_CR21","doi-asserted-by":"crossref","unstructured":"Yuan J, Song Z, Sun M, Wang X, Zhao WX (2021) Dual sparse attention network for session-based recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 4635\u20134643. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16593","DOI":"10.1609\/aaai.v35i5.16593"},{"key":"1702_CR22","doi-asserted-by":"publisher","unstructured":"Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, pp 285\u2013295. https:\/\/doi.org\/10.1145\/371920.372071","DOI":"10.1145\/371920.372071"},{"key":"1702_CR23","doi-asserted-by":"publisher","unstructured":"Jannach D, Ludewig M (2017) When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the eleventh ACM conference on recommender systems, pp 306\u2013310. https:\/\/doi.org\/10.1145\/3109859.3109872","DOI":"10.1145\/3109859.3109872"},{"key":"1702_CR24","doi-asserted-by":"publisher","unstructured":"Garg D, Gupta P, Malhotra P, Vig L, Shroff G (2019) Sequence and time aware neighborhood for session-based recommendations: Stan. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 1069\u20131072. https:\/\/doi.org\/10.1145\/3331184.3331322","DOI":"10.1145\/3331184.3331322"},{"key":"1702_CR25","doi-asserted-by":"publisher","unstructured":"Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Proceedings. 2005 IEEE International joint conference on neural networks, vol 2. IEEE, pp 729\u2013734. https:\/\/doi.org\/10.1109\/IJCNN.2005.1555942","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"1702_CR26","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International conference on machine learning. PMLR, pp 1263\u20131272. arXiv:1704.01212"},{"key":"1702_CR27","unstructured":"Li Y, Tarlow D, Brockschmidt M, Zemel R (2015) Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493"},{"key":"1702_CR28","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"issue":"4","key":"1702_CR29","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s11257-018-9209-6","volume":"28","author":"M Ludewig","year":"2018","unstructured":"Ludewig M, Jannach D (2018) Evaluation of session-based recommendation algorithms. User Model User-Adap Inter 28(4):331\u2013390","journal-title":"User Model User-Adap Inter"},{"key":"1702_CR30","doi-asserted-by":"publisher","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. https:\/\/doi.org\/10.3115\/v1\/d14-1179","DOI":"10.3115\/v1\/d14-1179"},{"key":"1702_CR31","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: ICML. https:\/\/icml.cc\/Conferences\/2010\/papers\/432.pdf"},{"key":"1702_CR32","doi-asserted-by":"crossref","unstructured":"Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P (2019) Bert4rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 1441\u20131450. arXiv:1904.06690","DOI":"10.1145\/3357384.3357895"},{"key":"1702_CR33","doi-asserted-by":"publisher","unstructured":"Guo L, Yin H, Wang Q, Chen T, Zhou A, Quoc Viet\u00a0Hung N (2019) Streaming session-based recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1569\u20131577. https:\/\/doi.org\/10.1145\/3292500.3330839","DOI":"10.1145\/3292500.3330839"},{"key":"1702_CR34","unstructured":"Vinyals O, Bengio S, Kudlur M (2015) Order matters: sequence to sequence for sets. arXiv preprint arXiv:1511.06391"},{"key":"1702_CR35","doi-asserted-by":"crossref","unstructured":"Xia X, Yin H, Yu J, Wang Q, Cui L, Zhang X (2021) Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 4503\u20134511","DOI":"10.1609\/aaai.v35i5.16578"},{"key":"1702_CR36","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.neucom.2021.11.068","volume":"472","author":"L Feng","year":"2022","unstructured":"Feng L, Cai Y, Wei E, Li J (2022) Graph neural networks with global noise filtering for session-based recommendation. Neurocomputing 472:113\u2013123","journal-title":"Neurocomputing"},{"key":"1702_CR37","unstructured":"Kingma DP, Ba J, Adam (2014) A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"1702_CR38","doi-asserted-by":"publisher","unstructured":"Wu L, Li S, Hsieh C-J, Sharpnack J (2020) Sse-pt: sequential recommendation via personalized transformer. In: Fourteenth ACM conference on recommender systems, pp 328\u2013337. https:\/\/doi.org\/10.1145\/3383313.3412258","DOI":"10.1145\/3383313.3412258"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01702-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01702-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01702-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T08:17:30Z","timestamp":1679127450000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01702-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,3]]},"references-count":38,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["1702"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01702-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2022,11,3]]},"assertion":[{"value":"27 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}