{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T01:55:16Z","timestamp":1782957316070,"version":"3.54.5"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"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":["Knowl Inf Syst"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s10115-023-01867-w","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T20:02:13Z","timestamp":1680724933000},"page":"3337-3352","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A new interest extraction method based on multi-head attention mechanism for CTR prediction"],"prefix":"10.1007","volume":"65","author":[{"given":"Haifeng","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linjing","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianghui","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yupeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xujun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"1867_CR1","doi-asserted-by":"crossref","unstructured":"Wang J, Huang P, Zhao H, Zhang Z, Zhao B, Lee DL (2018) Billion-scale commodity embedding for e-commerce recommendation in alibaba. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 839\u2013848","DOI":"10.1145\/3219819.3219869"},{"key":"1867_CR2","doi-asserted-by":"crossref","unstructured":"An M, Wu F, Wu C, Zhang K, Liu Z, Xie X (2019) Neural news recommendation with long- and short-term user representations. In: Proceedings of the 57th conference of the association for computational linguistics, pp 336\u2013345","DOI":"10.18653\/v1\/P19-1033"},{"key":"1867_CR3","doi-asserted-by":"crossref","unstructured":"Chen W, Huang P, Xu J, Guo, X, Guo C, Sun F, Li C, Pfadler A, Zhao H, Zhao B (2019) POG: personalized outfit generation for fashion recommendation at alibaba ifashion. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2662\u20132670","DOI":"10.1145\/3292500.3330652"},{"key":"1867_CR4","doi-asserted-by":"crossref","unstructured":"Ni Y, Ou D, Liu S, Li X, Ou W, Zeng A, Si L (2018) Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 596\u2013605","DOI":"10.1145\/3219819.3219828"},{"key":"1867_CR5","unstructured":"Pei C, Zhang Y, Zhang Y, Sun F, Pei D (2019) Personalized context-aware re-ranking for e-commerce recommender systems"},{"key":"1867_CR6","doi-asserted-by":"crossref","unstructured":"He X, Pan J, Jin O, Xu T, Liu B, Xu T, Shi Y, Atallah A, Herbrich R, Bowers S, Candela JQ (2014) Practical lessons from predicting clicks on ads at facebook. In: Proceedings of the eighth international workshop on data mining for online advertising, pp 5\u2013159","DOI":"10.1145\/2648584.2648589"},{"key":"1867_CR7","doi-asserted-by":"crossref","unstructured":"Huang Z, Pan Z, Liu Q, Long B, Ma H, Chen E (2017) An ad CTR prediction method based on feature learning of deep and shallow layers. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 2119\u20132122","DOI":"10.1145\/3132847.3133072"},{"key":"1867_CR8","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"1867_CR9","doi-asserted-by":"crossref","unstructured":"Lauriola I, Lavelli A, Aiolli F (2022) An introduction to deep learning in natural language processing: models, techniques, and tools. Neurocomputing, pp 443\u2013456","DOI":"10.1016\/j.neucom.2021.05.103"},{"key":"1867_CR10","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: human language technologies, pp 4171\u20134186"},{"key":"1867_CR11","doi-asserted-by":"crossref","unstructured":"Cheng H, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V, Liu X, Shah H (2016) Wide & deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems, pp 7\u201310","DOI":"10.1145\/2988450.2988454"},{"key":"1867_CR12","doi-asserted-by":"crossref","unstructured":"Qu Y, Cai H, Ren K, Zhang W, Yu Y, Wen Y, Wang J (2016) Product-based neural networks for user response prediction. In: IEEE 16th international conference on data mining, pp 1149\u20131154","DOI":"10.1109\/ICDM.2016.0151"},{"key":"1867_CR13","doi-asserted-by":"crossref","unstructured":"Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2018) Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1059\u20131068","DOI":"10.1145\/3219819.3219823"},{"key":"1867_CR14","doi-asserted-by":"crossref","unstructured":"Zhou G, Mou N, Fan Y, Pi Q, Bian W, Zhou C, Zhu X, Gai K (2019) Deep interest evolution network for click-through rate prediction. In: The thirty-third AAAI conference on artificial intelligence, pp 5941\u20135948","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"1867_CR15","doi-asserted-by":"crossref","unstructured":"Lyu Z, Dong Y, Huo C, Ren W Deep match to rank model for personalized click-through rate prediction. In: The thirty-fourth AAAI conference on artificial intelligence, pp 156\u2013163","DOI":"10.1609\/aaai.v34i01.5346"},{"key":"1867_CR16","doi-asserted-by":"crossref","unstructured":"McMahan HB, Hol G, Sculley D, Young M, Ebner D, Grady J, Nie L, Phillips T, Davydov E, Golovin D, Chikkerur S, Liu D, Wattenberg M, Hrafnkelsson AM, Boulos T, Kubica J (2013) Ad click prediction: a view from the trenches. In: The 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1222\u20131230","DOI":"10.1145\/2487575.2488200"},{"key":"1867_CR17","doi-asserted-by":"crossref","unstructured":"Rendle S (2010) Factorization machines. In: Webb GI, Liu B, Zhang C, Gunopulos D, Wu X (eds) ICDM 2010, The 10th IEEE international conference on data mining, Sydney, pp 995\u20131000","DOI":"10.1109\/ICDM.2010.127"},{"key":"1867_CR18","doi-asserted-by":"crossref","unstructured":"Juan Y, Zhuang Y, Chin W, Lin C (2016) Field-aware factorization machines for CTR prediction. In: Proceedings of the 10th ACM conference on recommender systems, pp 43\u201350","DOI":"10.1145\/2959100.2959134"},{"key":"1867_CR19","doi-asserted-by":"crossref","unstructured":"Pan J, Xu J, Ruiz AL, Zhao W, Pan S, Sun Y, Lu Q (2018) Field-weighted factorization machines for click-through rate prediction in display advertising. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp 1349\u20131357","DOI":"10.1145\/3178876.3186040"},{"key":"1867_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112846","volume":"139","author":"Y Yang","year":"2020","unstructured":"Yang Y, Cai J, Yang H, Zhang J, Zhao X (2020) TAD: a trajectory clustering algorithm based on spatial-temporal density analysis. Expert Syst Appl 139:112846","journal-title":"Expert Syst Appl"},{"key":"1867_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117018","volume":"201","author":"Y Yang","year":"2022","unstructured":"Yang Y, Cai J, Yang H, Li Y, Zhao X (2022) Isbfk-means: a new clustering algorithm based on influence space. Expert Syst Appl 201:117018","journal-title":"Expert Syst Appl"},{"key":"1867_CR22","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.ins.2022.03.027","volume":"596","author":"Y Yang","year":"2022","unstructured":"Yang Y, Cai J, Yang H, Zhao X (2022) Density clustering with divergence distance and automatic center selection. Inf Sci 596:414\u2013438","journal-title":"Inf Sci"},{"issue":"4","key":"1867_CR23","doi-asserted-by":"publisher","first-page":"5496","DOI":"10.1093\/mnras\/stac2975","volume":"517","author":"H Yang","year":"2022","unstructured":"Yang H, Shi C, Cai J, Zhou L, Yang Y, Zhao X, He Y, Hao J (2022) Data mining techniques on astronomical spectra data-i. clustering analysis. Monthly Notices Astron Soc 517(4):5496\u20135523","journal-title":"Monthly Notices Astron Soc"},{"issue":"4","key":"1867_CR24","doi-asserted-by":"publisher","first-page":"5904","DOI":"10.1093\/mnras\/stac3292","volume":"518","author":"H Yang","year":"2022","unstructured":"Yang H, Zhou L, Cai J, Shi C, Yang Y, Zhao X, Duan J, Yin X (2022) Data mining techniques on astronomical spectra data-ii. classification analysis. Monthly Notices R. Astron Soc 518(4):5904\u20135928","journal-title":"Monthly Notices R. Astron Soc"},{"key":"1867_CR25","doi-asserted-by":"crossref","unstructured":"He X, Chua T (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, Shinjuku, pp 355\u2013364","DOI":"10.1145\/3077136.3080777"},{"key":"1867_CR26","doi-asserted-by":"crossref","unstructured":"Xiao J, Ye H, He X, Zhang H, Wu F, Chua T (2017) Attentional factorization machines: learning the weight of feature interactions via attention networks. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 3119\u20133125","DOI":"10.24963\/ijcai.2017\/435"},{"key":"1867_CR27","doi-asserted-by":"crossref","unstructured":"Guo H, Tang R, Ye Y. Li Z, He X (2017) Deepfm: a factorization-machine based neural network for CTR prediction. In: Sierra, C. (ed.) Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp. 1725\u20131731","DOI":"10.24963\/ijcai.2017\/239"},{"key":"1867_CR28","doi-asserted-by":"crossref","unstructured":"Wang R, Fu B, Fu G, Wang M (2017) Deep & cross network for ad click predictions. In: Proceedings of the ADKDD\u201917, pp 12\u20131127","DOI":"10.1145\/3124749.3124754"},{"key":"1867_CR29","doi-asserted-by":"crossref","unstructured":"Lian J, Zhou X, Zhang F, Chen Z, Xie X, Sun G (2018) xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1754\u20131763","DOI":"10.1145\/3219819.3220023"},{"key":"1867_CR30","doi-asserted-by":"crossref","unstructured":"Chen Q, Zhao H, Li W, Huang P, Ou W (2019) Behavior sequence transformer for e-commerce recommendation in alibaba. In: Proceedings of the 1st international workshop on deep learning practice for high-dimensional sparse data, pp 1\u20134","DOI":"10.1145\/3326937.3341261"},{"key":"1867_CR31","doi-asserted-by":"crossref","unstructured":"Feng Y, Lv F, Shen W, Wang M, Sun F, Zhu Y, Yang K (2019) Deep session interest network for click-through rate prediction. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, pp 2301\u20132307","DOI":"10.24963\/ijcai.2019\/319"},{"key":"1867_CR32","doi-asserted-by":"crossref","unstructured":"Wu M, Xing J, Chen S (2022) Deep user multi-interest network for click-through rate prediction. In: knowledge science, engineering and management\u201415th international conference. lecture notes in computer science, vol 13369, pp 57\u201369","DOI":"10.1007\/978-3-031-10986-7_5"},{"key":"1867_CR33","doi-asserted-by":"crossref","unstructured":"Zhang K, Qian H, Cui Q, Liu Q, Li L, Zhou J, Ma J, Chen E (2021) Multi-interactive attention network for fine-grained feature learning in CTR prediction. In: WSDM \u201921, The fourteenth ACM international conference on web search and data mining, pp 984\u2013992","DOI":"10.1145\/3437963.3441761"},{"key":"1867_CR34","doi-asserted-by":"publisher","unstructured":"Yan C, Li X, Chen Y, Zhang Y (2022) JointCTR: a joint CTR prediction framework combining feature interaction and sequential behavior learning. Appl Intell 52, 4701\u20134714 (2022). https:\/\/doi.org\/10.1007\/s10489-021-02678-8","DOI":"10.1007\/s10489-021-02678-8"},{"key":"1867_CR35","doi-asserted-by":"crossref","unstructured":"Jiang W, Jiao Y, Wang Q, Liang C, Guo L, Zhang Y, Sun Z, Xiong Y, Zhu Y (2022) Triangle graph interest network for click-through rate prediction. In: Proceedings of the fifteenth ACM international conference on web search and data mining","DOI":"10.1145\/3488560.3498458"},{"key":"1867_CR36","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems 30: Annual conference on neural information processing systems 2017, pp 5998\u20136008"},{"issue":"7553","key":"1867_CR37","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton GE (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"1867_CR38","unstructured":"Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International conference on learning representations"},{"issue":"8","key":"1867_CR39","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit. Lett. 27(8):861\u2013874","journal-title":"Pattern Recognit. Lett."},{"key":"1867_CR40","unstructured":"Yan L, Li W, Xue G, Han D (2014) Coupled group lasso for web-scale CTR prediction in display advertising. In: Proceedings of the 31th international conference on machine learning. JMLR workshop and conference Proceedings, vol 32. pp 802\u2013810"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-023-01867-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-023-01867-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-023-01867-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T10:07:18Z","timestamp":1686305238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-023-01867-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,5]]},"references-count":40,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1867"],"URL":"https:\/\/doi.org\/10.1007\/s10115-023-01867-w","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,5]]},"assertion":[{"value":"25 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2023","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The study is original and has not been submitted to any other journal\/conference.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}