{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:32:06Z","timestamp":1786980726610,"version":"build-2736575974"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2021A1515011965"],"award-info":[{"award-number":["2021A1515011965"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976052"],"award-info":[{"award-number":["61976052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"National Science Fund for Excellent Young Scholars","doi-asserted-by":"crossref","award":["62122022"],"award-info":[{"award-number":["62122022"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong","doi-asserted-by":"publisher","award":["2021ZD0111501"],"award-info":[{"award-number":["2021ZD0111501"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-05851-x","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T02:39:25Z","timestamp":1732675165000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Time-aware tensor factorization for temporal recommendation"],"prefix":"10.1007","volume":"55","author":[{"given":"Yali","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruichu","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"5851_CR1","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.neunet.2022.11.032","volume":"159","author":"W Wang","year":"2023","unstructured":"Wang W, Wen W, Hao Z, Cai R (2023) Factorizing time-heterogeneous markov transition for temporal recommendation. Neural Netw 159:84\u201396","journal-title":"Neural Netw"},{"key":"5851_CR2","doi-asserted-by":"crossref","unstructured":"Gao X, Ma Z, Cui J, Xia X, Xu C (2023) Hierarchical category-enhanced prototype learning for imbalanced temporal recommendation. In: Proceedings of the 31st ACM international conference on multimedia, pp 6181\u20136189","DOI":"10.1145\/3581783.3613829"},{"key":"5851_CR3","doi-asserted-by":"publisher","first-page":"108239","DOI":"10.1016\/j.knosys.2022.108239","volume":"241","author":"X Huang","year":"2022","unstructured":"Huang X, Hou H, Sun M (2022) A novel temporal recommendation method based on user query topic evolution. Knowl Based Syst 241:108239","journal-title":"Knowl Based Syst"},{"key":"5851_CR4","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.neucom.2021.09.056","volume":"467","author":"X Liu","year":"2022","unstructured":"Liu X, Yang Y, Xu Y, Yang F, Huang Q, Wang H (2022) Real-time poi recommendation via modeling long-and short-term user preferences. Neurocomputing 467:454\u2013464","journal-title":"Neurocomputing"},{"key":"5851_CR5","doi-asserted-by":"crossref","unstructured":"Cho J, Hyun D, Kang S, Yu H (2021) Learning heterogeneous temporal patterns of user preference for timely recommendation. In: Proceedings of the web conference 2021, pp 1274\u20131283","DOI":"10.1145\/3442381.3449947"},{"key":"5851_CR6","doi-asserted-by":"crossref","unstructured":"Du Y, Liu H, Wu, Z (2021) Modeling multi-factor and multi-faceted preferences over sequential networks for next item recommendation. In: Proceedings of the 2021 European conference on machine learning and knowledge discovery in databases, pp 516\u2013531","DOI":"10.1007\/978-3-030-86520-7_32"},{"key":"5851_CR7","doi-asserted-by":"crossref","unstructured":"Wang J, Ding K, Hong L, Liu H, Caverlee J (2020) Next-item recommendation with sequential hypergraphs. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 1101\u20131110","DOI":"10.1145\/3397271.3401133"},{"key":"5851_CR8","doi-asserted-by":"crossref","unstructured":"Tanjim MM, Ayuubi HA, Cottrell GW (2020) Dynamicrec: a dynamic convolutional network for next item recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 2237\u20132240","DOI":"10.1145\/3340531.3412118"},{"key":"5851_CR9","doi-asserted-by":"crossref","unstructured":"Kang W-C, McAuley J (2018) Self-attentive sequential recommendation. In: Proceedings of 2018 IEEE the 18th international conference on data mining (ICDM), pp 197\u2013206","DOI":"10.1109\/ICDM.2018.00035"},{"issue":"10","key":"5851_CR10","doi-asserted-by":"publisher","first-page":"5125","DOI":"10.1109\/TNNLS.2021.3069058","volume":"33","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Cao L, Shi C, Niu Z (2021) Neural time-aware sequential recommendation by jointly modeling preference dynamics and explicit feature couplings. IEEE Trans Neural Netw Learn Syst 33(10):5125\u20135137","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5851_CR11","doi-asserted-by":"crossref","unstructured":"Wu X, Shi B, Dong Y, Huang C, Chawla NV (2019) Neural tensor factorization for temporal interaction learning. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 537\u2013545","DOI":"10.1145\/3289600.3290998"},{"key":"5851_CR12","doi-asserted-by":"crossref","unstructured":"Zhu T, Shi Y, Zhang Y, Wu Y, Mo F, Nie J-Y (2024) Collaboration and transition: Distilling item transitions into multi-query self-attention for sequential recommendation. In: Proceedings of the web conference 2024, pp 1003\u20131011","DOI":"10.1145\/3616855.3635787"},{"key":"5851_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44196-021-00022-z","volume":"14","author":"C Yin","year":"2021","unstructured":"Yin C, Chen Y, Zuo W (2021) Evolutionary social poisson factorizationfor temporal recommendation. Int J Comput Intell Syst 14:1\u201310","journal-title":"Int J Comput Intell Syst"},{"issue":"1","key":"5851_CR14","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/JSYST.2019.2900325","volume":"14","author":"J Chen","year":"2019","unstructured":"Chen J, Wei L, Liji U, Hao F (2019) A temporal recommendation mechanism based on signed network of user interest changes. IEEE Syst J 14(1):244\u2013252","journal-title":"IEEE Syst J"},{"issue":"4","key":"5851_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3057283","volume":"35","author":"X Li","year":"2017","unstructured":"Li X, Jiang M, Hong H, Liao L (2017) A time-aware personalized point-of-interest recommendation via high-order tensor factorization. ACM Trans Inf Syst (TOIS) 35(4):1\u201323","journal-title":"ACM Trans Inf Syst (TOIS)"},{"key":"5851_CR16","doi-asserted-by":"crossref","unstructured":"He R, Kang W-C, 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":"5851_CR17","unstructured":"Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: Successive point-of-interest recommendation. In: Proceedings of the 23rd international joint conference on artificial intelligence, pp 2605\u20132611"},{"key":"5851_CR18","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":"5851_CR19","doi-asserted-by":"crossref","unstructured":"Li P, Que M, Tuzhilin A (2023) Dual contrastive learning for efficient static feature representation in sequential recommendations. IEEE Trans Knowl Data Eng 544\u2013555","DOI":"10.1109\/TKDE.2023.3289469"},{"key":"5851_CR20","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) Fusing similarity models with markov chains for sparse sequential recommendation. In: Proceedings of 2016 IEEE the 16th international conference on data mining (ICDM), pp 191\u2013200","DOI":"10.1109\/ICDM.2016.0030"},{"key":"5851_CR21","doi-asserted-by":"crossref","unstructured":"Wang P, Guo J, Lan Y, Xu J, Wan S, Cheng X (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","DOI":"10.1145\/2766462.2767694"},{"key":"5851_CR22","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"},{"issue":"6088","key":"5851_CR23","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536","journal-title":"Nature"},{"key":"5851_CR24","doi-asserted-by":"crossref","unstructured":"Quadrana M, Karatzoglou A, Hidasi B, Cremonesi P (2017) Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the eleventh ACM conference on recommender systems, pp 130\u2013137","DOI":"10.1145\/3109859.3109896"},{"key":"5851_CR25","doi-asserted-by":"crossref","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","DOI":"10.1145\/3269206.3271761"},{"key":"5851_CR26","doi-asserted-by":"crossref","unstructured":"Beutel A, Covington P, Jain S, Xu C, Li J, Gatto V, Chi EH (2018) Latent cross: Making use of context in recurrent recommender systems. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 46\u201354","DOI":"10.1145\/3159652.3159727"},{"issue":"11","key":"5851_CR27","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"5","key":"5851_CR28","first-page":"4741","volume":"35","author":"M Zhang","year":"2022","unstructured":"Zhang M, Wu S, Yu X, Liu Q, Wang L (2022) Dynamic graph neural networks for sequential recommendation. IEEE Trans Knowl Data Eng 35(5):4741\u20134753","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5851_CR29","doi-asserted-by":"crossref","unstructured":"Chang J, Gao C, Zheng Y, Hui Y, Niu Y, Song Y, Jin D, Li Y (2021) Sequential recommendation with graph neural networks. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 378\u2013387","DOI":"10.1145\/3404835.3462968"},{"key":"5851_CR30","doi-asserted-by":"crossref","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","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"5851_CR31","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: Encoder-decoder approaches. In: Proceedings of SSST-8, eighth workshop on syntax, semantics and structure in statistical translation, pp 103\u2013111","DOI":"10.3115\/v1\/W14-4012"},{"key":"5851_CR32","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. In: Proceedings of the international conference on learning representations"},{"key":"5851_CR33","doi-asserted-by":"crossref","unstructured":"Hao Y, Zhang T, Zhao P, Liu Y, Sheng VS, Xu J, Liu G, Zhou X (2023) Feature-level deeper self-attention network with contrastive learning for sequential recommendation. IEEE Trans Knowl Data Eng 10112\u201310124","DOI":"10.1109\/TKDE.2023.3250463"},{"key":"5851_CR34","doi-asserted-by":"crossref","unstructured":"Du X, Yuan H, Zhao P, Qu J, Zhuang F, Liu G, Liu Y, Sheng VS (2023) Frequency enhanced hybrid attention network for sequential recommendation. In: Proceedings of the 46th International ACM SIGIR conference on research and development in information retrieval, pp 78\u201388","DOI":"10.1145\/3539618.3591689"},{"key":"5851_CR35","doi-asserted-by":"crossref","unstructured":"Zang R, Zuo M, Ma R (2023) Joint gaussian distribution and attention for time-aware recommendation systems. IEEE Trans Comput Soc Syst 1517\u20131526","DOI":"10.1109\/TCSS.2023.3315756"},{"key":"5851_CR36","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.neucom.2019.04.073","volume":"358","author":"Q Cui","year":"2019","unstructured":"Cui Q, Wu S, Huang Y, Wang L (2019) A hierarchical contextual attention-based network for sequential recommendation. Neurocomputing 358:141\u2013149","journal-title":"Neurocomputing"},{"key":"5851_CR37","doi-asserted-by":"crossref","unstructured":"Wang S, Hu L, Cao L, Huang X, Lian D, Liu W (2018) Attention-based transactional context embedding for next-item recommendation. In: Proceedings of the AAAI conference on artificial intelligence, pp 2532\u20132539","DOI":"10.1609\/aaai.v32i1.11851"},{"key":"5851_CR38","doi-asserted-by":"publisher","first-page":"109894","DOI":"10.1016\/j.asoc.2022.109894","volume":"133","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Yang B, Liu H, Li D (2023) A time-aware self-attention based neural network model for sequential recommendation. Appl Soft Comput 133:109894","journal-title":"Appl Soft Comput"},{"key":"5851_CR39","doi-asserted-by":"crossref","unstructured":"Costa FSd, Dolog P (2019) Collective embedding for neural context-aware recommender systems. In: Proceedings of the 13th ACM conference on recommender systems, pp 201\u2013209","DOI":"10.1145\/3298689.3347028"},{"key":"5851_CR40","doi-asserted-by":"crossref","unstructured":"Chen L, Yang N, Yu PS (2022) Time lag aware sequential recommendation. In: Proceedings of the 31st ACM international conference on information & knowledge management, pp 212\u2013221","DOI":"10.1145\/3511808.3557473"},{"key":"5851_CR41","doi-asserted-by":"crossref","unstructured":"Ji Y, Yin M, Fang Y, Yang H, Wang X, Jia T, Shi C (2021) Temporal heterogeneous interaction graph embedding for next-item recommendation. In: Proceedings of the 2020 european conference on machine learning and knowledge discovery in databases. Springer, Part III, pp 314\u2013329","DOI":"10.1007\/978-3-030-67664-3_19"},{"key":"5851_CR42","doi-asserted-by":"crossref","unstructured":"Li J, Wang Y, McAuley J (2020) Time interval aware self-attention for sequential recommendation. In: Proceedings of the 13th international conference on web search and data mining, pp 322\u2013330","DOI":"10.1145\/3336191.3371786"},{"key":"5851_CR43","doi-asserted-by":"crossref","unstructured":"Rendle S, Schmidt-Thieme L (2010) Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the third ACM international conference on web search and data mining, pp 81\u201390","DOI":"10.1145\/1718487.1718498"},{"key":"5851_CR44","doi-asserted-by":"crossref","unstructured":"Rendle S, Balby\u00a0Marinho L, Nanopoulos A, Schmidt-Thieme L (2009) Learning optimal ranking with tensor factorization for tag recommendation. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 727\u2013736","DOI":"10.1145\/1557019.1557100"},{"issue":"5","key":"5851_CR45","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1049\/iet-cvi.2018.5764","volume":"14","author":"Y Feng","year":"2020","unstructured":"Feng Y, Zhou G (2020) Orthogonal random projection for tensor completion. IET Comput Vis 14(5):233\u2013240","journal-title":"IET Comput Vis"},{"key":"5851_CR46","doi-asserted-by":"crossref","unstructured":"Feng Y, Zhou G, Qiu Y, Sun W (2018) Orthogonal random projection based tensor completion for image recovery. In: 2018 Asia-pacific signal and information processing association annual summit and conference (APSIPA ASC), pp 1350\u20131354","DOI":"10.23919\/APSIPA.2018.8659762"},{"issue":"11","key":"5851_CR47","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05851-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05851-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05851-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T10:11:02Z","timestamp":1735812662000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05851-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["5851"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05851-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"6 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"36"}}