{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T08:07:27Z","timestamp":1778314047300,"version":"3.51.4"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Shandong Nature Science Foundation of China","award":["ZR2020MF044"],"award-info":[{"award-number":["ZR2020MF044"]}]},{"name":"Shandong Nature Science Foundation of China","award":["ZR2020MF044"],"award-info":[{"award-number":["ZR2020MF044"]}]},{"name":"Shandong Nature Science Foundation of China","award":["ZR2020MF044"],"award-info":[{"award-number":["ZR2020MF044"]}]},{"name":"Shandong Nature Science Foundation of China","award":["ZR2020MF044"],"award-info":[{"award-number":["ZR2020MF044"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s10115-023-02057-4","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T09:03:12Z","timestamp":1706691792000},"page":"3231-3259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Position-category-aware attention network for next-item recommendation"],"prefix":"10.1007","volume":"66","author":[{"given":"Liqing","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingjian","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caixia","family":"Jing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuying","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"issue":"4","key":"2057_CR1","first-page":"851","volume":"42","author":"P He","year":"2019","unstructured":"He P, Wu H, Zeng C, Ma Y (2019) Truser: an approach to service recommendation based on trusted users. Chin J Comput 42(4):851\u2013863","journal-title":"Chin J Comput"},{"key":"2057_CR2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5457044","author":"J Dong","year":"2022","unstructured":"Dong J, Sun F, Wu T, Wu X, Zhang W, Wang S (2022) A hierarchical network with user memory matrix for long sequence recommendation. Wirel Commun Mob Comput. https:\/\/doi.org\/10.1155\/2022\/5457044","journal-title":"Wirel Commun Mob Comput"},{"key":"2057_CR3","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":"2057_CR4","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) Fusing similarity models with markov chains for sparse sequential recommendation. In: 2016 IEEE 16th international conference on data mining (ICDM). IEEE, pp 191\u2013200","DOI":"10.1109\/ICDM.2016.0030"},{"key":"2057_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9288902","author":"Y Zha","year":"2022","unstructured":"Zha Y, Zhang Y, Liu Z, Dong Y (2022) Self-attention based time-rating-aware context recommender system. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2022\/9288902","journal-title":"Comput Intell Neurosci"},{"key":"2057_CR6","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939"},{"key":"2057_CR7","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.neucom.2019.09.016","volume":"376","author":"Y Cao","year":"2019","unstructured":"Cao Y, Zhang W, Song B, Pan W, Xu C (2019) Position-aware context attention for session-based recommendation. Neurocomputing 376:65\u201372","journal-title":"Neurocomputing"},{"key":"2057_CR8","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: Tenth ACM international conference on web search data mining (2017)","DOI":"10.1145\/3018661.3018689"},{"key":"2057_CR9","doi-asserted-by":"crossref","unstructured":"Quadrana M, Karatzoglou A, Hidasi B, Cremonesi P (2017) Personalizing session-based recommendations with hierarchical recurrent neural networks. In: ACM","DOI":"10.1145\/3109859.3109896"},{"key":"2057_CR10","doi-asserted-by":"crossref","unstructured":"Jannach D, Ludewig M (2017) When recurrent neural networks meet the neighborhood for session-based recommendation. In: Eleventh ACM conference on recommender systems","DOI":"10.1145\/3109859.3109872"},{"key":"2057_CR11","doi-asserted-by":"crossref","unstructured":"Tang J, Ke W (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. In: ACM","DOI":"10.1145\/3159652.3159656"},{"key":"2057_CR12","doi-asserted-by":"crossref","unstructured":"Li J, Wang Y, Mcauley J (2020) Time interval aware self-attention for sequential recommendation. In: WSDM \u201920: The Thirteenth ACM international conference on web search and data mining","DOI":"10.1145\/3336191.3371786"},{"key":"2057_CR13","doi-asserted-by":"crossref","unstructured":"Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., Chua, T.S.: Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv:1708.04617 (2017)","DOI":"10.24963\/ijcai.2017\/435"},{"key":"2057_CR14","doi-asserted-by":"crossref","unstructured":"Tan Q, Zhang J, Liu N, Huang X, Hu X (2021) Dynamic memory based attention network for sequential recommendation. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v35i5.16564"},{"key":"2057_CR15","doi-asserted-by":"crossref","unstructured":"Cen Y, Zhang J, Zou X, Zhou C, Yang H, Tang J (2020) Controllable multi-interest framework for recommendation. In: Knowledge discovery and data mining","DOI":"10.1145\/3394486.3403344"},{"key":"2057_CR16","doi-asserted-by":"crossref","unstructured":"Kang WC, Mcauley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM)","DOI":"10.1109\/ICDM.2018.00035"},{"key":"2057_CR17","unstructured":"Zhang S, Tay Y, Yao L, Sun A, An J (2019) Next item recommendation with self-attentive metric learning. In: Thirty-Third AAAI conference on artificial intelligence, vol. 9"},{"key":"2057_CR18","doi-asserted-by":"crossref","unstructured":"Zhou G, Song C, Zhu X, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2017) Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining","DOI":"10.1145\/3219819.3219823"},{"key":"2057_CR19","doi-asserted-by":"crossref","unstructured":"Xu, W., He, H., Tan, M., Li, Y., Lang, J., Guo, D.: Deep interest with hierarchical attention network for click-through rate prediction. arXiv (2020)","DOI":"10.1145\/3397271.3401310"},{"key":"2057_CR20","doi-asserted-by":"crossref","unstructured":"Ying H, Zhuang F, Zhang F, Liu Y (2018) Sequential recommender system based on hierarchical attention network. In IJCAI international joint conference on artificial intelligence","DOI":"10.24963\/ijcai.2018\/546"},{"key":"2057_CR21","unstructured":"Zhang MH (2018) Stamp: Short-term attention\/memory priority model for session-based recommendation. SIGKDD explorations (Udisk). In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining"},{"key":"2057_CR22","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","DOI":"10.1145\/3357384.3357895"},{"key":"2057_CR23","doi-asserted-by":"crossref","unstructured":"Wu L, Li S, Hsieh C, Sharpnack J (2020) Sse-pt: Sequential recommendation via personalized transformer. In: RecSys \u201920: Fourteenth ACM conference on recommender systems","DOI":"10.1145\/3383313.3412258"},{"key":"2057_CR24","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neucom.2021.02.015","volume":"441","author":"J Zhang","year":"2021","unstructured":"Zhang J, Wang D, Yu D (2021) Tlsan: time-aware long- and short-term attention network for next-item recommendation. Neurocomputing 441:179\u2013191","journal-title":"Neurocomputing"},{"key":"2057_CR25","doi-asserted-by":"publisher","first-page":"166455","DOI":"10.1109\/ACCESS.2021.3135983","volume":"9","author":"L Niu","year":"2021","unstructured":"Niu L, Peng Y, Liu Y (2021) Deep recommendation model combining long- and short-term interest preferences. IEEE Access 9:166455\u2013166464","journal-title":"IEEE Access"},{"key":"2057_CR26","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv (2017)"},{"key":"2057_CR27","doi-asserted-by":"crossref","unstructured":"Zhou C, Bai J, Song J, Liu X, Zhao Z, Chen X, Gao J (2017) Atrank: an attention-based user behavior modeling framework for recommendation. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11618"},{"key":"2057_CR28","doi-asserted-by":"crossref","unstructured":"Huang X, Qian S, Quan F, Sang J, Xu C (2018) Csan: contextual self-attention network for user sequential recommendation. In: 2018 ACM multimedia conference (2018)","DOI":"10.1145\/3240508.3240609"},{"key":"2057_CR29","doi-asserted-by":"crossref","unstructured":"Yu Z, Lian J, Mahmoody A, Liu G, Xie X (2019) Adaptive user modeling with long and short-term preferences for personalized recommendation. In: International joint conference on artificial intelligence","DOI":"10.24963\/ijcai.2019\/585"},{"issue":"7","key":"2057_CR30","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1016\/j.engappai.2005.06.010","volume":"18","author":"M Papagelis","year":"2005","unstructured":"Papagelis M, Plexousakis D (2005) Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Eng Appl Artif Intell 18(7):781\u2013789","journal-title":"Eng Appl Artif Intell"},{"key":"2057_CR31","doi-asserted-by":"crossref","unstructured":"Yang X, Steck H, Yong L (2012) Circle-based recommendation in online social networks. In: ACM SIGKDD international conference on knowledge discovery and datamining","DOI":"10.1145\/2339530.2339728"},{"issue":"1","key":"2057_CR32","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"2057_CR33","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.90"},{"key":"2057_CR34","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr: Bayesian personalized ranking from implicit feedback. In: UAI 2009, Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, Montreal, QC, Canada, June 18\u201321"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-023-02057-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-023-02057-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-023-02057-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T04:10:58Z","timestamp":1716955858000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-023-02057-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,31]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["2057"],"URL":"https:\/\/doi.org\/10.1007\/s10115-023-02057-4","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,31]]},"assertion":[{"value":"25 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2024","order":4,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}