{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:44:53Z","timestamp":1777675493722,"version":"3.51.4"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031434112","type":"print"},{"value":"9783031434129","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43412-9_25","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T20:28:38Z","timestamp":1694896118000},"page":"422-437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Decompose, Then Reconstruct: A Framework of\u00a0Network Structures for\u00a0Click-Through Rate Prediction"],"prefix":"10.1007","author":[{"given":"Jiaming","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lang","family":"Lang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenlong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haozhao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruixuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenchao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"issue":"1","key":"25_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10589-022-00389-5","volume":"83","author":"E Birgin","year":"2022","unstructured":"Birgin, E., Mart\u00ednez, J.: Block coordinate descent for smooth nonconvex constrained minimization. Comput. Optim. Appl. 83(1), 1\u201327 (2022)","journal-title":"Comput. Optim. Appl."},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, W., Shen, Y., Huang, L.: Adaptive factorization network: learning adaptive-order feature interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3609\u20133616 (2020)","DOI":"10.1609\/aaai.v34i04.5768"},{"key":"25_CR3","unstructured":"Du, N., et al.: GLaM: efficient scaling of language models with mixture-of-experts. In: International Conference on Machine Learning, pp. 5547\u20135569. PMLR (2022)"},{"key":"25_CR4","doi-asserted-by":"publisher","unstructured":"Guo, H., TANG, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 1725\u20131731 (2017). https:\/\/doi.org\/10.24963\/ijcai.2017\/239","DOI":"10.24963\/ijcai.2017\/239"},{"key":"25_CR5","first-page":"15908","volume":"34","author":"K Han","year":"2021","unstructured":"Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. Adv. Neural. Inf. Process. Syst. 34, 15908\u201315919 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"He, X., Chua, T.S.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 355\u2013364 (2017)","DOI":"10.1145\/3077136.3080777"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Lang, L., Zhu, Z., Liu, X., Zhao, J., Xu, J., Shan, M.: Architecture and operation adaptive network for online recommendations. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3139\u20133149 (2021)","DOI":"10.1145\/3447548.3467133"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Lemons, S., L\u00f3pez, C.L., Holte, R.C., Ruml, W.: Beam search: faster and monotonic. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 32, pp. 222\u2013230 (2022)","DOI":"10.1609\/icaps.v32i1.19805"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Li, Z., Cheng, W., Chen, Y., Chen, H., Wang, W.: Interpretable click-through rate prediction through hierarchical attention. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 313\u2013321 (2020)","DOI":"10.1145\/3336191.3371785"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., Sun, G.: 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 (2018)","DOI":"10.1145\/3219819.3220023"},{"issue":"1","key":"25_CR11","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.ejor.2021.10.015","volume":"301","author":"L Libralesso","year":"2022","unstructured":"Libralesso, L., Focke, P.A., Secardin, A., Jost, V.: Iterative beam search algorithms for the permutation flowshop. Eur. J. Oper. Res. 301(1), 217\u2013234 (2022)","journal-title":"Eur. J. Oper. Res."},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Pan, J., Xu, J., Ruiz, A.L., Zhao, W., Pan, S., Sun, Y., Lu, Q.: Field-weighted factorization machines for click-through rate prediction in display advertising. In: Proceedings of the 2018 World Wide Web Conference, pp. 1349\u20131357 (2018)","DOI":"10.1145\/3178876.3186040"},{"issue":"1","key":"25_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/00220670209598786","volume":"96","author":"CYJ Peng","year":"2002","unstructured":"Peng, C.Y.J., Lee, K.L., Ingersoll, G.M.: An introduction to logistic regression analysis and reporting. J. Educ. Res. 96(1), 3\u201314 (2002)","journal-title":"J. Educ. Res."},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Qu, Y., Cai, H., Ren, K., Zhang, W., Yu, Y., Wen, Y., Wang, J.: Product-based neural networks for user response prediction. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1149\u20131154. IEEE (2016)","DOI":"10.1109\/ICDM.2016.0151"},{"key":"25_CR15","unstructured":"Rajbhandari, S., et al.: DeepSpeed-MoE: advancing mixture-of-experts inference and training to power next-generation AI scale. In: International Conference on Machine Learning, pp. 18332\u201318346. PMLR (2022)"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Rendle, S., Gantner, Z., Freudenthaler, C., Schmidt-Thieme, L.: Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 635\u2013644 (2011)","DOI":"10.1145\/2009916.2010002"},{"key":"25_CR17","first-page":"8583","volume":"34","author":"C Riquelme","year":"2021","unstructured":"Riquelme, C., et al.: Scaling vision with sparse mixture of experts. Adv. Neural. Inf. Process. Syst. 34, 8583\u20138595 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Sun, Y., Pan, J., Zhang, A., Flores, A.: FM2: field-matrixed factorization machines for recommender systems. In: Proceedings of the Web Conference 2021, pp. 2828\u20132837 (2021)","DOI":"10.1145\/3442381.3449930"},{"issue":"6","key":"25_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3530811","volume":"55","author":"Y Tay","year":"2022","unstructured":"Tay, Y., Dehghani, M., Bahri, D., Metzler, D.: Efficient transformers: a survey. ACM Comput. Surv. 55(6), 1\u201328 (2022)","journal-title":"ACM Comput. Surv."},{"issue":"3","key":"25_CR20","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1023\/A:1017501703105","volume":"109","author":"P Tseng","year":"2001","unstructured":"Tseng, P.: Convergence of a block coordinate descent method for nondifferentiable minimization. J. Optim. Theory Appl. 109(3), 475 (2001)","journal-title":"J. Optim. Theory Appl."},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Wang, R., Fu, B., Fu, G., Wang, M.: Deep & cross network for ad click predictions. In: Proceedings of the ADKDD\u201917, pp. 1\u20137 (2017)","DOI":"10.1145\/3124749.3124754"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Wang, R., Shivanna, R., Cheng, D., Jain, S., Lin, D., Hong, L., Chi, E.: DCN V2: improved deep & cross network and practical lessons for web-scale learning to rank systems. In: Proceedings of the web conference 2021, pp. 1785\u20131797 (2021)","DOI":"10.1145\/3442381.3450078"},{"key":"25_CR23","doi-asserted-by":"publisher","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. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 3119\u20133125 (2017). https:\/\/doi.org\/10.24963\/ijcai.2017\/435","DOI":"10.24963\/ijcai.2017\/435"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, J., et al.: BARS: towards open benchmarking for recommender systems. In: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR\u201922) (2022)","DOI":"10.1145\/3477495.3531723"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Zhu, J., Liu, J., Yang, S., Zhang, Q., He, X.: Open benchmarking for click-through rate prediction. In: The 30th ACM International Conference on Information and Knowledge Management (CIKM\u201921), pp. 2759\u20132769 (2021)","DOI":"10.1145\/3459637.3482486"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43412-9_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T20:34:14Z","timestamp":1694896454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43412-9_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434112","9783031434129"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43412-9_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This research work on feature interaction networks and click-through rate (CTR) prediction was conducted with a focus on developing a novel framework for improving the accuracy of CTR prediction models. The research work was conducted with adherence to ethical principles and standards of research integrity. The research does not involve any human subjects or any sensitive data, and all the data are evaluated from the most mainstream public datasets in CTR prediction task, so no ethical approval is required. The research work was conducted with the aim of advancing the state-of-the-art in CTR prediction models, and the results of this study can have potential implications for businesses and industries that rely on CTR prediction models. The authors acknowledge the contributions of prior research in this area and have given appropriate credit to previous works. The authors have also disclosed any potential conflicts of interest related to this research work. The research work was conducted with transparency and openness, and has been peer-reviewed and vetted.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}