{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T04:19:28Z","timestamp":1784261968707,"version":"3.55.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031429408","type":"print"},{"value":"9783031429415","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\/978-3-031-42941-5_51","type":"book-chapter","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T08:02:46Z","timestamp":1693382566000},"page":"583-590","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Systematic Literature Review on\u00a0Click Through Rate Prediction"],"prefix":"10.1007","author":[{"given":"Paulina","family":"Leszcze\u0142owska","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Bollin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Grabski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"51_CR1","doi-asserted-by":"crossref","unstructured":"Deng, H., Wang, Y., Luo, J., Hu, J.: Similitude attentive relation network for click-through rate prediction. In: 2020 International Joint Conference on Neural Networks (IJCNN) (2020)","DOI":"10.1109\/IJCNN48605.2020.9207521"},{"key":"51_CR2","doi-asserted-by":"crossref","unstructured":"Di, S.: Deep interest network for taobao advertising data click-through rate prediction. In: 2021 International Conference on Communications, Information System and Computer Engineering (CISCE) (2021)","DOI":"10.1109\/CISCE52179.2021.9445990"},{"key":"51_CR3","doi-asserted-by":"crossref","unstructured":"Guo, W., et al.: Miss: Multi-interest self-supervised learning framework for click-through rate prediction. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE) (2022)","DOI":"10.1109\/ICDE53745.2022.00059"},{"key":"51_CR4","doi-asserted-by":"crossref","unstructured":"Inoue, D., Matsumoto, S.: Predicting CTR of regional flyer images using CNN. In: 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI) (2022)","DOI":"10.1109\/IIAIAAI55812.2022.00107"},{"key":"51_CR5","doi-asserted-by":"crossref","unstructured":"Jiang, Z., et al.: A CTR prediction approach for advertising based on embedding model and deep learning. In: 2018 IEEE Intl Conference on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom) (2018)","DOI":"10.1109\/BDCloud.2018.00112"},{"key":"51_CR6","doi-asserted-by":"crossref","unstructured":"Karpus, A., Raczy\u0144ska, M., Przybylek, A.: Things you might not know about the k-nearest neighbors algorithm. In: 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (2019)","DOI":"10.5220\/0008365005390547"},{"key":"51_CR7","doi-asserted-by":"crossref","unstructured":"Li, L.S., Hong, J., Min, S., Xue, Y.: A novel CTR prediction model based on deepfm for taobao data. In: 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) (2021)","DOI":"10.1109\/AIID51893.2021.9456556"},{"key":"51_CR8","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, Z., Wu, X., Yuan, B., Wang, X.: A dual adaptive factorization network for CTR prediction. 2021 IEEE 23rd International Conference on High Performance Computing & Communications; 7th International Conference on Data Science & Systems; 19th International Conference on Smart City; 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC\/DSS\/SmartCity\/DependSys) (2021)","DOI":"10.1109\/HPCC-DSS-SmartCity-DependSys53884.2021.00122"},{"key":"51_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, Y., Chen, C., Huang, J.: CTR prediction with user behavior: An augmented method of deep factorization machines. In: 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (2019)","DOI":"10.1109\/ISKE47853.2019.9170417"},{"key":"51_CR10","doi-asserted-by":"crossref","unstructured":"Liu, S., Chen, D., Shao, J.: Ada: adaptive depth attention model for click - through rate prediction. In: 2021 International Joint Conference on Neural Networks (IJCNN) (2021)","DOI":"10.1109\/IJCNN52387.2021.9533867"},{"key":"51_CR11","doi-asserted-by":"crossref","unstructured":"Ni, Z., Ma, X., Sun, X., Bian, L.: A click prediction model based on residual unit with inception module. In: PRICAI 2019: Trends in Artificial Intelligence, pp. 393\u2013403 (2019)","DOI":"10.1007\/978-3-030-29911-8_30"},{"key":"51_CR12","doi-asserted-by":"crossref","unstructured":"Niu, T., Hou, Y.: Density matrix based convolutional neural network for click-through rate prediction. 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD) (2020)","DOI":"10.1109\/ICAIBD49809.2020.9137448"},{"key":"51_CR13","doi-asserted-by":"crossref","unstructured":"K. Potdar, T. S., and C. D. A comparative study of categorical variable encoding techniques for neural network classifiers. Int. J. Comput. Appl. 175(4), 7\u20139 (2017)","DOI":"10.5120\/ijca2017915495"},{"key":"51_CR14","doi-asserted-by":"crossref","unstructured":"Qiu, X., Zuo, Y., Liu, G.: Etcf: An ensemble model for CTR prediction. In: 2018 15th International Conference on Service Systems and Service Management (ICSSSM) (2018)","DOI":"10.1109\/ICSSSM.2018.8465044"},{"key":"51_CR15","doi-asserted-by":"crossref","unstructured":"She, X., Wang, S.: Research on advertising click-through rate prediction based on CNN-FM hybrid model. In: 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (2018)","DOI":"10.1109\/IHMSC.2018.10119"},{"key":"51_CR16","doi-asserted-by":"crossref","unstructured":"Shi, X., Yang, Y., Tao, C.: Deep interest network for taobao advertising data click-through rate prediction. CTR prediction model considering the importance of embedding vector. In: 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) (2021)","DOI":"10.1109\/ICAICA52286.2021.9498074"},{"key":"51_CR17","doi-asserted-by":"crossref","unstructured":"Wang, G., Wang, X.: Time-aware multi-layer interest extraction network for click-through rate prediction. In: 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) (2022)","DOI":"10.1109\/CACML55074.2022.00136"},{"key":"51_CR18","doi-asserted-by":"crossref","unstructured":"Wang, P., Sun, M., Wang, Z., Zhou, Y.: A novel CTR prediction based model using xdeepfm network. 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) (2021)","DOI":"10.1109\/CEI52496.2021.9574529"},{"key":"51_CR19","doi-asserted-by":"crossref","unstructured":"Wang, R., Guo, P., Fan, X., Li, B., Zhang, W., Xin, X.: Listwise click-through rate prediction with item-item interactions. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2018)","DOI":"10.1109\/SMC.2018.00746"},{"key":"51_CR20","doi-asserted-by":"crossref","unstructured":"Xia, B., Wang, X., Yamasaki, T., Aizawa, K., Seshime, H.: Deep neural network-based click-through rate prediction using multimodal features of online banners. In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) (2019)","DOI":"10.1109\/BigMM.2019.00-29"},{"key":"51_CR21","doi-asserted-by":"crossref","unstructured":"Xie, Y., Li, M., Lu, K., Shah, S.B., Zheng, X.: Multi-task learning model based on multiple characteristics and multiple interests for CTR prediction. In: 2022 IEEE Conference on Dependable and Secure Computing (DSC) (2022)","DOI":"10.1109\/DSC54232.2022.9888898"},{"key":"51_CR22","doi-asserted-by":"crossref","unstructured":"Xu, J., Shi, X., Qiao, H., Shang, M., He, X., HeQ.: Uein: A user evolving interests network for click-through rate prediction. In: 2021 IEEE 15th International Conference on Big Data Science and Engineering (BigDataSE) (2021)","DOI":"10.1109\/BigDataSE53435.2021.00015"},{"key":"51_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1007\/978-3-030-75765-6_35","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"R Yu","year":"2021","unstructured":"Yu, R., et al.: XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction. In: Karlapalem, K., et al. (eds.) PAKDD 2021. LNCS (LNAI), vol. 12713, pp. 436\u2013447. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-75765-6_35"},{"key":"51_CR24","doi-asserted-by":"crossref","unstructured":"Yuan, H., He, C.: Click-through rate prediction model based on dynamic graph attention mechanism network. In: 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE) (2021)","DOI":"10.1109\/RCAE53607.2021.9638842"},{"key":"51_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, Y.: CTR prediction model using xdeepfm and bayesian optimization. In: 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) (2021)","DOI":"10.1109\/CSAIEE54046.2021.9543277"},{"key":"51_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Yang, S., Liu, G., Feng, D., Xu, K.: Fint: Field-aware interaction neural network for click-through rate prediction. ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)","DOI":"10.1109\/ICASSP43922.2022.9747247"},{"key":"51_CR27","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018)","DOI":"10.1145\/3219819.3219823"},{"key":"51_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, X., Shi, Y.: Deepfafm :a field-array factorization machine based neural network for CTR prediction. In: 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (2020)","DOI":"10.1109\/ITNEC48623.2020.9084654"},{"key":"51_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, T., Li, S., Liang, C., Liu, B., Li, X.: Product click-through rate prediction model integrating self-attention mechanism. In: 2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC) (2021)","DOI":"10.1109\/CTISC52352.2021.00056"}],"container-title":["Communications in Computer and Information Science","New Trends in Database and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42941-5_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:31:30Z","timestamp":1710268290000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42941-5_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031429408","9783031429415"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42941-5_51","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADBIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Advances in Databases and Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Barcelona","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"4 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adbis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adbis.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"77","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":"14","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":"25","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":"18% - 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":"N\/A","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":"N\/A","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)"}}]}}