{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:30:56Z","timestamp":1783791056116,"version":"3.55.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976178"],"award-info":[{"award-number":["61976178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076202"],"award-info":[{"award-number":["62076202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s00521-020-05643-7","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T15:12:28Z","timestamp":1609773148000},"page":"11091-11105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Attentive Hybrid Recurrent Neural Networks for sequential recommendation"],"prefix":"10.1007","volume":"33","author":[{"given":"Lixiang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peisen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingchen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2180-8941","authenticated-orcid":false,"given":"Haobin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"5643_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.eswa.2017.06.020","volume":"87","author":"RC Bagher","year":"2017","unstructured":"Bagher RC, Hassanpour H, Mashayekhi H (2017) User trends modeling for a content-based recommender system. Expert Syst Appl 87:209\u2013219","journal-title":"Expert Syst Appl"},{"key":"5643_CR2","first-page":"109","volume":"46","author":"J Bobadilla","year":"2013","unstructured":"Bobadilla J, Ortega F, Hernando A, Guti\u00e9rrez A (2013) Knowledge-based systems. Recommender Syst Surv 46:109\u2013132","journal-title":"Recommender Syst Surv"},{"key":"5643_CR3","unstructured":"Chen L, Zhang G, Zhou E (2018) Fast greedy map inference for determinantal point process to improve recommendation diversity. In: Advances in Neural Information Processing Systems, pp. 5622\u20135633"},{"key":"5643_CR4","doi-asserted-by":"crossref","unstructured":"Chen S, Xu J, Joachims T (2013) Multi-space probabilistic sequence modeling. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 865\u2013873","DOI":"10.1145\/2487575.2487632"},{"key":"5643_CR5","doi-asserted-by":"crossref","unstructured":"Cho K, van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder\u2013decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724\u20131734","DOI":"10.3115\/v1\/D14-1179"},{"key":"5643_CR6","unstructured":"Choi S, Ha H, Hwang U, Kim C, Ha JW, Yoon S (2018) Reinforcement learning based recommender system using biclustering technique. arXiv preprint arXiv:1801.05532"},{"key":"5643_CR7","unstructured":"Devooght R, Bersini H (2016) Collaborative filtering with recurrent neural networks. arXiv preprint arXiv:1608.07400"},{"key":"5643_CR8","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5643_CR9","doi-asserted-by":"crossref","unstructured":"He R, Kang WC, McAuley J (2017) Translation-based recommendation. In: Proceedings of the eleventh ACM conference on recommender systems, pp. 161\u2013169","DOI":"10.1145\/3109859.3109882"},{"key":"5643_CR10","doi-asserted-by":"crossref","unstructured":"He X, Chua TS (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 355\u2013364","DOI":"10.1145\/3077136.3080777"},{"key":"5643_CR11","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: 4th International Conference on Learning Representations, ICLR 2016"},{"key":"5643_CR12","doi-asserted-by":"crossref","unstructured":"Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263\u2013272. Ieee","DOI":"10.1109\/ICDM.2008.22"},{"issue":"12","key":"5643_CR13","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.3390\/app8122426","volume":"8","author":"R Huang","year":"2018","unstructured":"Huang R, McIntyre S, Song M, Ou Z et al (2018) An attention-based recommender system to predict contextual intent based on choice histories across and within sessions. Appl Sci 8(12):2426","journal-title":"Appl Sci"},{"key":"5643_CR14","doi-asserted-by":"crossref","unstructured":"Kabbur S, Ning X, Karypis G (2013) Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 659\u2013667","DOI":"10.1145\/2487575.2487589"},{"key":"5643_CR15","doi-asserted-by":"crossref","unstructured":"Kang WC, McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197\u2013206. IEEE","DOI":"10.1109\/ICDM.2018.00035"},{"key":"5643_CR16","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"5643_CR17","doi-asserted-by":"crossref","unstructured":"Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426\u2013434","DOI":"10.1145\/1401890.1401944"},{"key":"5643_CR18","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-1-4899-7637-6_3","volume-title":"Recommender systems handbook","author":"Y Koren","year":"2015","unstructured":"Koren Y, Bell R (2015) Advances in collaborative filtering. In: Recommender systems handbook. Springer, Berlin, pp 77\u2013118"},{"issue":"8","key":"5643_CR19","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30\u201337","journal-title":"Computer"},{"key":"5643_CR20","doi-asserted-by":"crossref","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","DOI":"10.1145\/3132847.3132926"},{"key":"5643_CR21","doi-asserted-by":"crossref","unstructured":"Linden, G., Smith, B., York, J.: Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7(1), 76\u201380 (2003)","DOI":"10.1109\/MIC.2003.1167344"},{"key":"5643_CR22","doi-asserted-by":"crossref","unstructured":"Ling G, Lyu MR, King I (2014) Ratings meet reviews, a combined approach to recommend. In: Proceedings of the 8th ACM Conference on Recommender systems, pp. 105\u2013112","DOI":"10.1145\/2645710.2645728"},{"key":"5643_CR23","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.dss.2015.03.008","volume":"74","author":"J Lu","year":"2015","unstructured":"Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12\u201332","journal-title":"Decis Support Syst"},{"key":"5643_CR24","doi-asserted-by":"crossref","unstructured":"McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on Recommender systems, pp. 165\u2013172","DOI":"10.1145\/2507157.2507163"},{"key":"5643_CR25","unstructured":"Van\u00a0den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. In: Advances in neural information processing systems, pp. 2643\u20132651"},{"key":"5643_CR26","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618"},{"key":"5643_CR27","doi-asserted-by":"crossref","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","DOI":"10.1145\/1772690.1772773"},{"key":"5643_CR28","doi-asserted-by":"crossref","unstructured":"Ruocco M, Skrede OSL, Langseth H (2017) Inter-session modeling for session-based recommendation. In: Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, pp. 24\u201331","DOI":"10.1145\/3125486.3125491"},{"key":"5643_CR29","doi-asserted-by":"crossref","unstructured":"Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th international conference on Machine learning, pp. 880\u2013887","DOI":"10.1145\/1390156.1390267"},{"key":"5643_CR30","doi-asserted-by":"crossref","unstructured":"Salakhutdinov R, Mnih A, Hinton G (2007) Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on Machine learning, pp. 791\u2013798","DOI":"10.1145\/1273496.1273596"},{"key":"5643_CR31","doi-asserted-by":"crossref","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","DOI":"10.1145\/371920.372071"},{"key":"5643_CR32","first-page":"8875","volume":"975","author":"A Singhal","year":"2017","unstructured":"Singhal A, Sinha P, Pant R (2017) Use of deep learning in modern recommendation system: a summary of recent works. Int J Comp Appl 975:8875\u20138887","journal-title":"Int J Comp Appl"},{"key":"5643_CR33","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","DOI":"10.1145\/3357384.3357895"},{"key":"5643_CR34","doi-asserted-by":"crossref","unstructured":"Tang J, Wang K (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565\u2013573","DOI":"10.1145\/3159652.3159656"},{"key":"5643_CR35","doi-asserted-by":"crossref","unstructured":"Terzi M, Rowe M, Ferrario MA, Whittle J(2014) Text-based user-knn: Measuring user similarity based on text reviews. In: International Conference on User Modeling, Adaptation, and Personalization, pp. 195\u2013206. Springer","DOI":"10.1007\/978-3-319-08786-3_17"},{"key":"5643_CR36","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"},{"key":"5643_CR37","unstructured":"Wang S, Cao L, Wang Y (2019) A survey on session-based recommender systems. arXiv preprint arXiv:1902.04864"},{"key":"5643_CR38","doi-asserted-by":"crossref","unstructured":"Wang X, Wang Y (2014) Improving content-based and hybrid music recommendation using deep learning. In: Proceedings of the 22nd ACM international conference on Multimedia, pp. 627\u2013636","DOI":"10.1145\/2647868.2654940"},{"key":"5643_CR39","doi-asserted-by":"crossref","unstructured":"Xiao J, Ye H, He X, Zhang H, Wu F, Chua TS (2017) Attentional factorization machines: learning the weight of feature interactions via attention networks. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 3119\u20133125","DOI":"10.24963\/ijcai.2017\/435"},{"key":"5643_CR40","doi-asserted-by":"crossref","unstructured":"Yao L, Sheng QZ, Ngu AH, Ashman H, Li X (2014) Exploring recommendations in internet of things. In: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp. 855\u2013858","DOI":"10.1145\/2600428.2609458"},{"issue":"1","key":"5643_CR41","doi-asserted-by":"publisher","first-page":"776","DOI":"10.2991\/ijcis.2017.10.1.52","volume":"10","author":"R Yera","year":"2017","unstructured":"Yera R, Martinez L (2017) Fuzzy tools in recommender systems: a survey. Int J Comput Intell Syst 10(1):776\u2013803","journal-title":"Int J Comput Intell Syst"},{"key":"5643_CR42","doi-asserted-by":"crossref","unstructured":"Yu F, Liu Q, Wu S, Wang L, Tan T (2016) A dynamic recurrent model for next basket recommendation. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 729\u2013732","DOI":"10.1145\/2911451.2914683"},{"key":"5643_CR43","doi-asserted-by":"crossref","unstructured":"Zhao X, Zhang L, Ding Z, Xia L, Tang J, Yin D (2018) Recommendations with negative feedback via pairwise deep reinforcement learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1040\u20131048","DOI":"10.1145\/3219819.3219886"},{"key":"5643_CR44","doi-asserted-by":"crossref","unstructured":"Zhao Z, Hong L, Wei L, Chen J, Nath A, Andrews S, Kumthekar A, Sathiamoorthy M, Yi X, Chi E (2019) Recommending what video to watch next: a multitask ranking system. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 43\u201351","DOI":"10.1145\/3298689.3346997"},{"key":"5643_CR45","doi-asserted-by":"crossref","unstructured":"Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 425\u2013434","DOI":"10.1145\/3018661.3018665"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05643-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05643-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05643-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T11:56:09Z","timestamp":1670673369000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05643-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":45,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["5643"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05643-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]},"assertion":[{"value":"19 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}