{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:31:16Z","timestamp":1778603476782,"version":"3.51.4"},"reference-count":35,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Click-through rate (CTR) prediction is a term used to predict the probability of a user clicking on an ad or item and has become a popular research area in advertising. As the volume of Internet data increases, the labor costs of traditional feature engineering continue to rise. To reduce the dependence on feature interactions, this paper proposes a fusion model that combines explicit and implicit feature interactions, called the Two-Tower Multi-Head Attention Neural Network (TMH) approach. The model integrates multiple components such as multi-head attention, residual network, and deep neural networks into an end-to-end model that automatically obtains vector-level combinations of explicit and implicit features to predict click-through rates through higher-order explicit and implicit interactions. We evaluated the effectiveness of TMH in CTR prediction through numerous experiments using three real datasets. The results demonstrate that our proposed method not only outperforms existing prediction methods but also offers good interpretability.<\/jats:p>","DOI":"10.1371\/journal.pone.0295440","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T17:36:42Z","timestamp":1710524202000},"page":"e0295440","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["TMH: Two-Tower Multi-Head Attention neural network for CTR prediction"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4877-8817","authenticated-orcid":true,"given":"Zijian","family":"An","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8435-0395","authenticated-orcid":true,"given":"Inwhee","family":"Joe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"issue":"2","key":"pone.0295440.ref001","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jik.2020.08.001","article-title":"Using data sciences in digital marketing: Framework, methods, and performance metrics","volume":"6","author":"JR Saura","year":"2021","journal-title":"Journal of Innovation & Knowledge"},{"key":"pone.0295440.ref002","doi-asserted-by":"crossref","unstructured":"Grbovic M, Radosavljevic V, Djuric N, et al. E-commerce in your inbox: Product recommendations at scale. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 2015: 1809\u20131818.","DOI":"10.1145\/2783258.2788627"},{"key":"pone.0295440.ref003","first-page":"13548565231161810","article-title":"If you press this, I\u2019ll pay: MrBeast, YouTube, and the mobilization of the audience commodity in the name of charity","author":"V Miller","year":"2023","journal-title":"Convergence"},{"issue":"5","key":"pone.0295440.ref004","first-page":"1","volume":"54","author":"D Jannach","year":"2021","journal-title":"A survey on conversational recommender systems ACM Computing Surveys (CSUR)"},{"key":"pone.0295440.ref005","doi-asserted-by":"crossref","unstructured":"Wang Y, Qin Y, Sun F, et al. DisenCTR: Dynamic graph-based disentangled representation for click-through rate prediction. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022: 2314\u20132318.","DOI":"10.1145\/3477495.3531851"},{"key":"pone.0295440.ref006","article-title":"A survey of collaborative filtering techniques","volume":"2009","author":"X Su","year":"2009","journal-title":"Advances in artificial intelligence"},{"key":"pone.0295440.ref007","doi-asserted-by":"crossref","unstructured":"Guo W, Su R, Tan R, et al. Dual graph enhanced embedding neural network for CTR prediction. Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 2021: 496\u2013504.","DOI":"10.1145\/3447548.3467384"},{"key":"pone.0295440.ref008","doi-asserted-by":"crossref","unstructured":"He X, Zhang H, Kan MY, et al. Fast matrix factorization for online recommendation with implicit feedback. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 2016: 549\u2013558.","DOI":"10.1145\/2911451.2911489"},{"key":"pone.0295440.ref009","doi-asserted-by":"crossref","unstructured":"Cheng HT, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems. Proceedings of the 1st workshop on deep learning for recommender systems. 2016: 7\u201310.","DOI":"10.1145\/2988450.2988454"},{"key":"pone.0295440.ref010","doi-asserted-by":"crossref","unstructured":"Liu H, Wen J, Jing L, et al. Deep generative ranking for personalized recommendation. Proceedings of the 13th ACM Conference on Recommender Systems. 2019: 34\u201342.","DOI":"10.1145\/3298689.3347012"},{"key":"pone.0295440.ref011","doi-asserted-by":"crossref","unstructured":"Rendle S. Factorization machines. 2010 IEEE International conference on data mining. IEEE, 2010: 995\u20131000.","DOI":"10.1109\/ICDM.2010.127"},{"key":"pone.0295440.ref012","doi-asserted-by":"crossref","unstructured":"Juan Y, Zhuang Y, Chin WS, et al. Field-aware factorization machines for CTR prediction. Proceedings of the 10th ACM conference on recommender systems. 2016: 43\u201350.","DOI":"10.1145\/2959100.2959134"},{"key":"pone.0295440.ref013","doi-asserted-by":"crossref","unstructured":"Kim M, Lee S, Kim J. Combining multiple implicit-explicit interactions for regression analysis. 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020: 74\u201383.","DOI":"10.1109\/BigData50022.2020.9378402"},{"key":"pone.0295440.ref014","unstructured":"Zhang W, Du T, Wang J. Deep Learning over Multi-field Categorical Data: \u2013A Case Study on User Response Prediction. Advances in Information Retrieval: 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20\u201323, 2016. Proceedings 38. Springer International Publishing, 2016: 45\u201357."},{"key":"pone.0295440.ref015","doi-asserted-by":"crossref","unstructured":"Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction. 2016 IEEE 16th international conference on data mining (ICDM). IEEE, 2016: 1149\u20131154.","DOI":"10.1109\/ICDM.2016.0151"},{"key":"pone.0295440.ref016","doi-asserted-by":"crossref","unstructured":"Guo H, Tang R, Ye Y, et al. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247, 2017.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"pone.0295440.ref017","doi-asserted-by":"crossref","unstructured":"Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions. Proceedings of the ADKDD\u201917. 2017: 1\u20137.","DOI":"10.1145\/3124749.3124754"},{"key":"pone.0295440.ref018","doi-asserted-by":"crossref","unstructured":"Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617, 2017.","DOI":"10.24963\/ijcai.2017\/435"},{"key":"pone.0295440.ref019","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony N, Campbell S, Carvalho A, et al. Deep learning vs. traditional computer vision. Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 1 1. Springer International Publishing, 2020: 128\u2013144.","DOI":"10.1007\/978-3-030-17795-9_10"},{"issue":"2","key":"pone.0295440.ref020","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1109\/TNNLS.2020.2979670","article-title":"A survey of the usages of deep learning for natural language processing","volume":"32","author":"DW Otter","year":"2020","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"pone.0295440.ref021","article-title":"Higher-order factorization machines","volume":"29","author":"M Blondel","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"pone.0295440.ref022","doi-asserted-by":"crossref","unstructured":"Song W, Shi C, Xiao Z, et al. Autoint: Automatic feature interaction learning via self-attentive neural networks. Proceedings of the 28th ACM international conference on information and knowledge management. 2019: 1161\u20131170.","DOI":"10.1145\/3357384.3357925"},{"key":"pone.0295440.ref023","doi-asserted-by":"crossref","unstructured":"Huang T, Zhang Z, Zhang J. FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction. Proceedings of the 13th ACM Conference on Recommender Systems. 2019: 169\u2013177.","DOI":"10.1145\/3298689.3347043"},{"key":"pone.0295440.ref024","unstructured":"Yan, Yachen and Li, Liubo J. xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions. arXiv preprint arXiv:2301.01089."},{"issue":"1","key":"pone.0295440.ref025","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s11063-022-10889-4","article-title":"HoINT: Learning Explicit and Implicit High-order Feature Interactions for Click-through Rate Prediction","volume":"55","author":"H Dong","year":"2023","journal-title":"Neural Processing Letters"},{"key":"pone.0295440.ref026","doi-asserted-by":"crossref","unstructured":"Lu W, Yu Y, Chang Y, et al. A dual input-aware factorization machine for CTR prediction. Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. 2021: 3139\u20133145.","DOI":"10.24963\/ijcai.2020\/434"},{"key":"pone.0295440.ref027","doi-asserted-by":"crossref","unstructured":"Yu Y, Wang Z, Yuan B. An Input-aware Factorization Machine for Sparse Prediction. IJCAI. 2019: 1466\u20131472.","DOI":"10.24963\/ijcai.2019\/203"},{"key":"pone.0295440.ref028","doi-asserted-by":"crossref","first-page":"104914","DOI":"10.1109\/ACCESS.2022.3211656","article-title":"A CTR Prediction Model With Double Matrix-Level Cross-Features","volume":"10","author":"W Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"pone.0295440.ref029","doi-asserted-by":"crossref","first-page":"3189","DOI":"10.1007\/s10489-020-01951-6","article-title":"Modeling low-and high-order feature interactions with FM and self-attention network","volume":"51","author":"C Yan","year":"2021","journal-title":"Applied Intelligence"},{"key":"pone.0295440.ref030","article-title":"Attention is all you need","volume":"30","author":"A Vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"pone.0295440.ref031","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et al. Identity mappings in deep residual networks. Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14. Springer International Publishing, 2016: 630\u2013645.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"pone.0295440.ref032","doi-asserted-by":"crossref","unstructured":"Shan Y, Hoens TR, Jiao J, et al. Deep crossing: Web-scale modeling without manually crafted combinatorial features. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016: 255\u2013262.","DOI":"10.1145\/2939672.2939704"},{"key":"pone.0295440.ref033","doi-asserted-by":"crossref","unstructured":"He X, Chua TS. Neural factorization machines for sparse predictive analytics. Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 2017: 355\u2013364.","DOI":"10.1145\/3077136.3080777"},{"key":"pone.0295440.ref034","doi-asserted-by":"crossref","unstructured":"Lian J, Zhou X, Zhang F, et al. xdeepfm: Combining explicit and implicit feature interactions for recommender systems. Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018: 1754\u20131763.","DOI":"10.1145\/3219819.3220023"},{"key":"pone.0295440.ref035","doi-asserted-by":"crossref","unstructured":"Abadi M. TensorFlow: learning functions at scale. Proceedings of the 21st ACM SIGPLAN international conference on functional programming. 2016: 1\u20131.","DOI":"10.1145\/2951913.2976746"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0295440","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T17:37:18Z","timestamp":1710524238000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0295440"}},"subtitle":[],"editor":[{"given":"AL","family":"MAHFOODH","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,3,15]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3,15]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0295440","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,15]]}}}