{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:54:48Z","timestamp":1776786888647,"version":"3.51.2"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T00:00:00Z","timestamp":1693612800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T00:00:00Z","timestamp":1693612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Tianjin \u201cProject + Team\u201d Key Training Project","award":["No.XC202022"],"award-info":[{"award-number":["No.XC202022"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Sci. Eng."],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Recently, many deep learning-based models have been successfully applied to click-through rate prediction. However, most previous models focus only on feature-level interactions between a single user behavior and the target item or only treat the user\u2019s historical behavior as a sequence to uncover the hidden interests behind it when mining user interests. This can lead to user interest that evolves over time dynamically being ignored or the interest shown by a single user\u2019s behavior not being exploited. Based on the above problems, we propose evolving interest with feature co-action network (EIFCN). Specifically, we first design user dynamic interest network to treat the user\u2019s historical behavior as a sequence of information, and tap into the user\u2019s hidden interests over time. In this part, we use a multi-head self-attention mechanism to initially process the data and then pass it into the deep learning network. Then a feature co-action network is designed to mine the user\u2019s single behavior and the displayed feature-level interactions of the target item. Experimental results show that the EIFCN model performs better than other models.<\/jats:p>","DOI":"10.1007\/s41019-023-00217-8","type":"journal-article","created":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T01:02:07Z","timestamp":1693616527000},"page":"344-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Evolving Interest with Feature Co-action Network for CTR Prediction"],"prefix":"10.1007","volume":"8","author":[{"given":"Zhiyang","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenguang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peilin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingbo","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5711-8638","authenticated-orcid":false,"given":"Yingyuan","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,2]]},"reference":[{"key":"217_CR1","doi-asserted-by":"publisher","unstructured":"Rendle S (2010) Factorization machines. In: Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.) ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14-17 December 2010, pp 995\u20131000. https:\/\/doi.org\/10.1109\/ICDM.2010.127","DOI":"10.1109\/ICDM.2010.127"},{"key":"217_CR2","doi-asserted-by":"publisher","unstructured":"Juan Y, Zhuang Y, Chin W, Lin C (2016) Field-aware factorization machines for CTR prediction. In: Sen S, Geyer W, Freyne J, Castells P (eds.) Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016, pp 43\u201350. https:\/\/doi.org\/10.1145\/2959100.2959134","DOI":"10.1145\/2959100.2959134"},{"key":"217_CR3","first-page":"102589","volume":"65","author":"A Sb","year":"2020","unstructured":"Sb A, Pkrm A, Qvp B, Trg A, Srks A, Clc A, Ma C, Mjp D (2020) Deep learning and medical image processing for coronavirus (covid-19) pandemic: a survey. Sustain Cities Soc 65:102589","journal-title":"Sustain Cities Soc"},{"issue":"1","key":"217_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s41746-020-00376-2","volume":"4","author":"A Esteva","year":"2021","unstructured":"Esteva A, Chou K, Yeung S, Naik N, Socher R (2021) Deep learning-enabled medical computer vision. NPJ Digital Med 4(1):5","journal-title":"NPJ Digital Med"},{"key":"217_CR5","doi-asserted-by":"publisher","first-page":"3383","DOI":"10.1007\/s11831-020-09504-3","volume":"28","author":"S Xu","year":"2020","unstructured":"Xu S, Wang J, Shou W, Ngo T, Wang X (2020) Computer vision techniques in construction: a critical review. Arch Comput Methods Eng 28:3383\u201397","journal-title":"Arch Comput Methods Eng"},{"issue":"10","key":"217_CR6","doi-asserted-by":"publisher","first-page":"4291","DOI":"10.1109\/TNNLS.2020.3019893","volume":"32","author":"A Galassi","year":"2021","unstructured":"Galassi A, Lippi M, Torroni P (2021) Attention in natural language processing. IEEE Trans Neural Netw Learn Syst 32(10):4291\u20134308","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"217_CR7","doi-asserted-by":"crossref","unstructured":"Liu J, Xiao Y, Zheng W, Hsu C-H (2022) Siga: social influence modeling integrating graph autoencoder for rating prediction. Appl Intell 1\u201316","DOI":"10.1007\/s10489-022-03748-1"},{"key":"217_CR8","doi-asserted-by":"publisher","first-page":"107777","DOI":"10.1016\/j.asoc.2021.107777","volume":"112","author":"Y Xiao","year":"2021","unstructured":"Xiao Y, Liu C, Zheng W, Wang H, Hsu C-H (2021) A feature interaction learning approach for crowdfunding project recommendation. Appl Soft Comput 112:107777","journal-title":"Appl Soft Comput"},{"key":"217_CR9","doi-asserted-by":"publisher","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: Karatzoglou A, Hidasi B, Tikk D, Shalom OS, Roitman H, Shapira B, Rokach L (eds.) Proceedings of the 1st workshop on deep learning for recommender systems, DLRS@RecSys 2016, Boston, MA, USA, September 15, 2016, pp 7\u201310. https:\/\/doi.org\/10.1145\/2988450.2988454","DOI":"10.1145\/2988450.2988454"},{"key":"217_CR10","doi-asserted-by":"publisher","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, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pp 1725\u20131731. https:\/\/doi.org\/10.24963\/ijcai.2017\/239","DOI":"10.24963\/ijcai.2017\/239"},{"key":"217_CR11","doi-asserted-by":"publisher","unstructured":"Wang R, Fu B, Fu G, Wang M (2017) Deep & cross network for ad click predictions. In: Proceedings of the ADKDD\u201917, Halifax, NS, Canada, August 13 - 17, 2017, pp 12\u20131127. https:\/\/doi.org\/10.1145\/3124749.3124754","DOI":"10.1145\/3124749.3124754"},{"key":"217_CR12","doi-asserted-by":"publisher","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: Guo Y, Farooq F (eds.) Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, KDD 2018, London, UK, August 19-23, 2018, pp 1754\u20131763. https:\/\/doi.org\/10.1145\/3219819.3220023","DOI":"10.1145\/3219819.3220023"},{"key":"217_CR13","doi-asserted-by":"publisher","unstructured":"Song W, Shi C, Xiao Z, Duan Z, Xu Y, Zhang M, Tang J (2019) Autoint: automatic feature interaction learning via self-attentive neural networks. In: Zhu W, Tao D, Cheng X, Cui P, Rundensteiner EA, Carmel D, He Q, Yu JX (eds.) Proceedings of the 28th ACM international conference on information and knowledge management, CIKM 2019, Beijing, China, November 3-7, 2019, pp 1161\u20131170. https:\/\/doi.org\/10.1145\/3357384.3357925","DOI":"10.1145\/3357384.3357925"},{"key":"217_CR14","doi-asserted-by":"publisher","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: Guo, Y., Farooq, F. (eds.) Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, KDD 2018, London, UK, August 19-23, 2018, pp 1059\u20131068. https:\/\/doi.org\/10.1145\/3219819.3219823","DOI":"10.1145\/3219819.3219823"},{"key":"217_CR15","doi-asserted-by":"publisher","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, AAAI 2019, the thirty-first innovative applications of artificial intelligence conference, iaai 2019, the ninth aaai symposium on educational advances in artificial intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pp 5941\u20135948. https:\/\/doi.org\/10.1609\/aaai.v33i01.33015941","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"217_CR16","doi-asserted-by":"publisher","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: Kraus, S. (ed.) proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pp 2301\u20132307. https:\/\/doi.org\/10.24963\/ijcai.2019\/319","DOI":"10.24963\/ijcai.2019\/319"},{"key":"217_CR17","doi-asserted-by":"publisher","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: Bonchi, F., Domingo-Ferrer, J., Baeza-Yates, R., Zhou, Z., Wu, X. (eds.) IEEE 16th international conference on data mining, ICDM 2016, December 12\u201315, 2016, Barcelona, Spain, pp 1149\u20131154. https:\/\/doi.org\/10.1109\/ICDM.2016.0151","DOI":"10.1109\/ICDM.2016.0151"},{"key":"217_CR18","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neunet.2019.09.020","volume":"121","author":"Y Yang","year":"2020","unstructured":"Yang Y, Xu B, Shen S, Shen F, Zhao J (2020) Operation-aware neural networks for user response prediction. Neural Netw 121:161\u2013168","journal-title":"Neural Netw"},{"key":"217_CR19","doi-asserted-by":"crossref","unstructured":"Cheng W, Shen Y, Huang L (2020) Adaptive factorization network: learning adaptive-order feature interactions. In: the thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, the thirty-second innovative applications of artificial intelligence conference, IAAI 2020, the tenth AAAI symposium on educational advances in artificial intelligence, EAAI 2020, New York, NY, USA, February 7\u201312, 2020, pp 3609\u20133616. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/5768","DOI":"10.1609\/aaai.v34i04.5768"},{"key":"217_CR20","doi-asserted-by":"publisher","unstructured":"Liu B, Zhu C, Li G, Zhang W, Lai J, Tang R, He X, Li Z, Yu Y (2020) Autofis: automatic feature interaction selection in factorization models for click-through rate prediction. In: Gupta R, Liu Y, Tang J, Prakash BA (eds.) KDD \u201920: the 26th ACM SIGKDD conference on knowledge discovery and data mining, virtual event, CA, USA, August 23\u201327, 2020, pp 2636\u20132645. https:\/\/doi.org\/10.1145\/3394486.3403314","DOI":"10.1145\/3394486.3403314"},{"key":"217_CR21","doi-asserted-by":"publisher","unstructured":"Li Z, Cui Z, Wu S, Zhang X, Wang L (2019) Fi-gnn: modeling feature interactions via graph neural networks for CTR prediction. In: Zhu, W., Tao, D., Cheng, X., Cui, P., Rundensteiner, E.A., Carmel, D., He, Q., Yu, J.X. (eds.) proceedings of the 28th ACM international conference on information and knowledge management, CIKM 2019, Beijing, China, November 3\u20137, 2019, pp 539\u2013548. https:\/\/doi.org\/10.1145\/3357384.3357951","DOI":"10.1145\/3357384.3357951"},{"key":"217_CR22","doi-asserted-by":"publisher","unstructured":"Bian W, Wu K, Ren L, Pi Q, Zhang Y, Xiao C, Sheng X, Zhu Y, Chan Z, Mou N, Luo X, Xiang S, Zhou G, Zhu X, Deng H (2022) CAN: feature co-action network for click-through rate prediction. In: Candan, K.S., Liu, H., Akoglu, L., Dong, X.L., Tang, J. (eds.) WSDM \u201922: the fifteenth ACM international conference on web search and data mining, virtual event\/tempe, AZ, USA, February 21\u201325, 2022, pp 57\u201365. https:\/\/doi.org\/10.1145\/3488560.3498435","DOI":"10.1145\/3488560.3498435"},{"key":"217_CR23","unstructured":"Xiong C, Merity S, Socher R (2016) Dynamic memory networks for visual and textual question answering. In: Balcan, M., Weinberger, K.Q. (eds.) proceedings of the 33nd international conference on machine learning, ICML 2016, New York City, NY, USA, June 19\u201324, 2016. JMLR Workshop and Conference Proceedings, vol. 48, pp. 2397\u20132406. http:\/\/proceedings.mlr.press\/v48\/xiong16.html"},{"key":"217_CR24","doi-asserted-by":"publisher","unstructured":"Li C, Liu Z, Wu M, Xu Y, Zhao H, Huang P, Kang G, Chen Q, Li W, Lee DL (2019) Multi-interest network with dynamic routing for recommendation at tmall. In: Zhu W, Tao D, Cheng X, Cui P, Rundensteiner EA, Carmel D, He Q, Yu JX (eds.) proceedings of the 28th ACM international conference on information and knowledge management, CIKM 2019, Beijing, China, November 3\u20137, 2019, pp 2615\u20132623. https:\/\/doi.org\/10.1145\/3357384.3357814","DOI":"10.1145\/3357384.3357814"},{"key":"217_CR25","doi-asserted-by":"publisher","unstructured":"Pi Q, Bian W, Zhou G, Zhu X, Gai K (2019) Practice on long sequential user behavior modeling for click-through rate prediction. In: Teredesai A, Kumar V, Li Y, Rosales R, Terzi E, Karypis G (eds.) Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, KDD 2019, Anchorage, AK, USA, August 4\u20138, 2019, pp 2671\u20132679. https:\/\/doi.org\/10.1145\/3292500.3330666","DOI":"10.1145\/3292500.3330666"},{"key":"217_CR26","doi-asserted-by":"publisher","unstructured":"Xu W, He H, Tan M, Li Y, Lang J, Guo D (2020) Deep interest with hierarchical attention network for click-through rate prediction. In: Huang, J.X., Chang, Y., Cheng, X., Kamps, J., Murdock, V., Wen, J., Liu, Y. (eds.) proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, SIGIR 2020, Virtual Event, China, July 25\u201330, 2020, pp 1905\u20131908. https:\/\/doi.org\/10.1145\/3397271.3401310","DOI":"10.1145\/3397271.3401310"},{"key":"217_CR27","doi-asserted-by":"publisher","unstructured":"Xiao Z, Yang L, Jiang W, Wei Y, Hu Y, Wang H (2020) Deep multi-interest network for click-through rate prediction. In: d\u2019Aquin, M., Dietze, S., Hauff, C., Curry, E., Cudr\u00e9-Mauroux, P. (eds.) CIKM \u201920: The 29th ACM international conference on information and knowledge management, virtual event, Ireland, October 19\u201323, 2020, pp 2265\u20132268. https:\/\/doi.org\/10.1145\/3340531.3412092","DOI":"10.1145\/3340531.3412092"},{"key":"217_CR28","doi-asserted-by":"publisher","unstructured":"Huang Z, Tao M, Zhang B (2021) Deep user match network for click-through rate prediction. In: Diaz F, Shah C, Suel T, Castells P, Jones R, Sakai T (eds.) SIGIR \u201921: The 44th international ACM SIGIR conference on research and development in information retrieval, virtual event, Canada, July 11\u201315, 2021, pp 1890\u20131894. https:\/\/doi.org\/10.1145\/3404835.3463078","DOI":"10.1145\/3404835.3463078"},{"key":"217_CR29","doi-asserted-by":"publisher","unstructured":"Zhang J, Lin F, Yang C, Wang W (2022) Deep multi-representational item network for CTR prediction. In: Amig\u00f3 E, Castells P, Gonzalo J, Carterette B, Culpepper JS, Kazai G (eds.) SIGIR \u201922: The 45th international ACM SIGIR conference on research and development in information retrieval, Madrid, Spain, July 11\u201315, 2022, pp 2277\u20132281. https:\/\/doi.org\/10.1145\/3477495.3531845","DOI":"10.1145\/3477495.3531845"},{"key":"217_CR30","doi-asserted-by":"crossref","unstructured":"Huang T, Zhang Z, Zhang J (2019) Fibinet: combining feature importance and bilinear feature interaction for click-through rate prediction. In: proceedings of the 13th ACM conference on recommender systems, RecSys 2019, Copenhagen, Denmark, September 16\u201320, 2019, pp 169\u2013177","DOI":"10.1145\/3298689.3347043"},{"key":"217_CR31","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., Garnett, R. (eds.) Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4\u20139, 2017, Long Beach, CA, USA, pp 5998\u20136008"},{"key":"217_CR32","unstructured":"Lin Z, Feng M, dos Santos CN, Yu M, Xiang B, Zhou B, Bengio Y (2017) A structured self-attentive sentence embedding. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, conference track proceedings (2017)"},{"key":"217_CR33","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2017) Graph attention networks. CoRR arxiv:https:\/\/arxiv.org\/abs\/1710.10903"},{"key":"217_CR34","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, USA, June 27\u201330, 2016, pp 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"217_CR35","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R (2012) Improving neural networks by preventing co-adaptation of feature detectors. CoRR arxiv:abs\/1207.0580"},{"key":"217_CR36","unstructured":"Ba LJ, Kiros JR, Hinton GE (2016) Layer normalization. CoRR arXiv:abs\/1607.06450"}],"container-title":["Data Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-023-00217-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41019-023-00217-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-023-00217-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T03:56:13Z","timestamp":1696996573000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41019-023-00217-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,2]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["217"],"URL":"https:\/\/doi.org\/10.1007\/s41019-023-00217-8","relation":{},"ISSN":["2364-1185","2364-1541"],"issn-type":[{"value":"2364-1185","type":"print"},{"value":"2364-1541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,2]]},"assertion":[{"value":"17 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 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":"All authors declare that there is no conflict of\ninterest regarding the publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}