{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T20:25:17Z","timestamp":1774124717412,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s13042-023-02085-0","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T19:01:51Z","timestamp":1711738911000},"page":"3143-3155","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Long-short interest network with graph-based method for sequential recommendation"],"prefix":"10.1007","volume":"15","author":[{"given":"Wangdong","family":"Mu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8195-1304","authenticated-orcid":false,"given":"Qihe","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongrong","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Zhuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"2085_CR1","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv preprint arXiv:1607.06450"},{"key":"2085_CR2","doi-asserted-by":"publisher","unstructured":"Chen X, Xu H, Zhang Y, et\u00a0al (2018) Sequential recommendation with user memory networks. In: Proceedings of the Eleventh ACM International Conference on web search and data mining, pp 108\u2013116, https:\/\/doi.org\/10.1145\/3159652.3159668","DOI":"10.1145\/3159652.3159668"},{"key":"2085_CR3","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, et\u00a0al (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"2085_CR4","doi-asserted-by":"publisher","unstructured":"Feng Y, Lv F, Shen W, et\u00a0al (2019) Deep session interest network for click-through rate prediction. In: IJCAI, https:\/\/doi.org\/10.24963\/ijcai.2019\/319","DOI":"10.24963\/ijcai.2019\/319"},{"key":"2085_CR5","doi-asserted-by":"publisher","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge discovery and data mining, pp 855\u2013864, https:\/\/doi.org\/10.1145\/2939672.2939754","DOI":"10.1145\/2939672.2939754"},{"key":"2085_CR6","doi-asserted-by":"publisher","DOI":"10.1145\/2827872","author":"FM Harper","year":"2015","unstructured":"Harper FM, Konstan JA (2015) The movielens datasets: history and context. Acm Trans Interact Intell Syst (tiis). https:\/\/doi.org\/10.1145\/2827872","journal-title":"Acm Trans Interact Intell Syst (tiis)"},{"key":"2085_CR7","doi-asserted-by":"publisher","unstructured":"he k, zhang x, ren s, et\u00a0al (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR) pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2085_CR8","doi-asserted-by":"publisher","unstructured":"He R, Kang WC, McAuley J (2017) Translation-based recommendation. In: Proceedings of the Eleventh ACM Conference on recommender systems, pp 161\u2013169, https:\/\/doi.org\/10.1145\/3109859.3109882","DOI":"10.1145\/3109859.3109882"},{"key":"2085_CR9","doi-asserted-by":"publisher","unstructured":"He R, Ravula A, Kanagal B, et\u00a0al (2021) Realformer: transformer likes residual attention. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 pp 929\u2013943. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.81","DOI":"10.18653\/v1\/2021.findings-acl.81"},{"key":"2085_CR10","doi-asserted-by":"publisher","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, https:\/\/doi.org\/10.1145\/3077136.3080777","DOI":"10.1145\/3077136.3080777"},{"key":"2085_CR11","doi-asserted-by":"publisher","unstructured":"He X, Zhang H, Kan MY, et\u00a0al (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR Conference on research and development in information retrieval, pp 549\u2013558, https:\/\/doi.org\/10.1145\/2911451.2911489","DOI":"10.1145\/2911451.2911489"},{"key":"2085_CR12","doi-asserted-by":"publisher","unstructured":"Hidasi B, Karatzoglou A (2018) Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM International Conference on information and knowledge management, pp 843\u2013852, https:\/\/doi.org\/10.1145\/3269206.3271761","DOI":"10.1145\/3269206.3271761"},{"key":"2085_CR13","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, et\u00a0al (2016) Session-based recommendations with recurrent neural networks. In: International Conference on learning representations"},{"key":"2085_CR14","doi-asserted-by":"publisher","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, et\u00a0al (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580https:\/\/doi.org\/10.48550\/arXiv.1207.0580","DOI":"10.48550\/arXiv.1207.0580"},{"issue":"8","key":"2085_CR15","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"2085_CR16","doi-asserted-by":"publisher","unstructured":"J\u00e4rvelin K, Kek\u00e4l\u00e4inen J (2017) Ir evaluation methods for retrieving highly relevant documents. In: ACM SIGIR Forum, ACM New York, NY, USA, pp 243\u2013250, https:\/\/doi.org\/10.1145\/3130348.3130374","DOI":"10.1145\/3130348.3130374"},{"key":"2085_CR17","doi-asserted-by":"publisher","unstructured":"Kang WC, McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE International Conference on data mining (ICDM), IEEE, pp 197\u2013206, https:\/\/doi.org\/10.1109\/ICDM.2018.00035","DOI":"10.1109\/ICDM.2018.00035"},{"key":"2085_CR18","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR arXiv: abs\/1412.6980"},{"issue":"8","key":"2085_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. https:\/\/doi.org\/10.1109\/MC.2009.263","journal-title":"Computer"},{"issue":"1","key":"2085_CR20","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","volume":"7","author":"G Linden","year":"2003","unstructured":"Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76\u201380","journal-title":"IEEE Internet Comput"},{"key":"2085_CR21","doi-asserted-by":"publisher","unstructured":"Liu Q, Zeng Y, Mokhosi R, et\u00a0al (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":"2085_CR22","doi-asserted-by":"publisher","unstructured":"McAuley J, Targett C, Shi Q, et\u00a0al (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on research and development in information retrieval, pp 43\u201352, https:\/\/doi.org\/10.1145\/2766462.2767755","DOI":"10.1145\/2766462.2767755"},{"key":"2085_CR23","unstructured":"Mikolov T, Chen K, Corrado G, et\u00a0al (2013) Efficient estimation of word representations in vector space. CoRR"},{"key":"2085_CR24","first-page":"2204","volume-title":"In: Proceedings of the 27th International Conference on Neural Information Processing Systems","author":"V Mnih","year":"2014","unstructured":"Mnih V, Heess N, Graves A et al (2014) Recurrent models of visual attention. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol. 2. MIT Press, NIPS\u201914, pp 2204\u20132212"},{"key":"2085_CR25","doi-asserted-by":"publisher","unstructured":"Perozzi B, Al-Rfou\u2019 R, Skiena S (2014) Deepwalk: online learning of social representations. In: KDD, pp 701\u2013710. https:\/\/doi.org\/10.1145\/2623330.2623732","DOI":"10.1145\/2623330.2623732"},{"key":"2085_CR26","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":"2085_CR27","unstructured":"Rendle S, Freudenthaler C, Gantner Z, et\u00a0al (2012) Bpr: Bayesian personalized ranking from implicit feedback. In: UAI \u201909 Proceedings of the Twenty-Fifth Conference on uncertainty in artificial intelligence, pp 452\u2013461"},{"key":"2085_CR28","doi-asserted-by":"publisher","unstructured":"Resnick P, Iacovou N, Suchak M, et\u00a0al (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on computer supported cooperative work, pp 175\u2013186, https:\/\/doi.org\/10.1145\/192844.192905","DOI":"10.1145\/192844.192905"},{"key":"2085_CR29","doi-asserted-by":"publisher","unstructured":"Sarwar B, Karypis G, Konstan J, et\u00a0al (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":"2085_CR30","doi-asserted-by":"publisher","unstructured":"Sedhain S, Menon AK, Sanner S, et\u00a0al (2015) Autorec: Autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, pp 111\u2013112, https:\/\/doi.org\/10.1145\/2740908.2742726","DOI":"10.1145\/2740908.2742726"},{"key":"2085_CR31","unstructured":"Shen S, Yao Z, Gholami A, et\u00a0al (2020) Powernorm: rethinking batch normalization in transformers. In: International Conference on machine learning, PMLR, pp 8741\u20138751"},{"key":"2085_CR32","doi-asserted-by":"publisher","unstructured":"Tang J, Wang K (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. In: WSDM 2018: the Eleventh ACM International Conference on Web Search and Data Mining Marina Del Rey CA USA February, 2018 pp 565\u2013573. https:\/\/doi.org\/10.1145\/3159652.3159656","DOI":"10.1145\/3159652.3159656"},{"key":"2085_CR33","doi-asserted-by":"publisher","unstructured":"Tang J, Qu M, Wang M, et\u00a0al (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on world wide web, pp 1067\u20131077, https:\/\/doi.org\/10.1145\/2736277.2741093","DOI":"10.1145\/2736277.2741093"},{"key":"2085_CR34","unstructured":"Vaswani A, Shazeer N, Parmar N, et\u00a0al (2017) Attention is all you need. In: Advances in neural information processing systems 30 (NIPS 2017), pp 5998\u20136008"},{"key":"2085_CR35","doi-asserted-by":"publisher","unstructured":"Wang P, Guo J, Lan Y, et\u00a0al (2015) Learning hierarchical representation model for nextbasket recommendation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 403\u2013412, https:\/\/doi.org\/10.1145\/2766462.2767694","DOI":"10.1145\/2766462.2767694"},{"key":"2085_CR36","doi-asserted-by":"publisher","unstructured":"Wang S, Hu L, Wang Y, et\u00a0al (2019) Sequential recommender systems-challenges, progress and prospects. In: IJCAI pp 6332\u20136338. https:\/\/doi.org\/10.48550\/arXiv.1902.04864","DOI":"10.48550\/arXiv.1902.04864"},{"key":"2085_CR37","doi-asserted-by":"publisher","unstructured":"Wu L, Li S, Hsieh CJ, et\u00a0al (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"},{"key":"2085_CR38","first-page":"10890","volume":"34","author":"L Wu","year":"2021","unstructured":"Wu L, Li J, Wang Y et al (2021) R-drop: Regularized dropout for neural networks. Adv Neural Inf Proc Syst 34:10890\u201310905","journal-title":"Adv Neural Inf Proc Syst"},{"key":"2085_CR39","doi-asserted-by":"publisher","unstructured":"Xiao J, Ye H, He X, et\u00a0al (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, https:\/\/doi.org\/10.24963\/ijcai.2017\/435","DOI":"10.24963\/ijcai.2017\/435"},{"key":"2085_CR40","doi-asserted-by":"publisher","unstructured":"Xue HJ, Dai X, Zhang J, et\u00a0al (2017) Deep matrix factorization models for recommender systems. In: IJCAI, Melbourne, Australia, pp 3203\u20133209, https:\/\/doi.org\/10.24963\/ijcai.2017\/447","DOI":"10.24963\/ijcai.2017\/447"},{"key":"2085_CR41","doi-asserted-by":"publisher","unstructured":"Zhou G, Mou N, Fan Y, et\u00a0al (2019) Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI Conference on artificial intelligence, pp 5941\u20135948, https:\/\/doi.org\/10.1609\/aaai.v33i01.33015941","DOI":"10.1609\/aaai.v33i01.33015941"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02085-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02085-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02085-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T07:38:03Z","timestamp":1719992283000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02085-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,29]]},"references-count":41,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["2085"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02085-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,29]]},"assertion":[{"value":"2 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}