Abstract
The ability to anticipate whether a user will click on an item is one of the most crucial aspects of operating an e-commerce business, and clickthrough rate prediction is an attempt to provide an answer to this question. Beginning with the simplest multilayer perceptrons and progressing to the most sophisticated attention networks, researchers employ a variety of methods to solve this issue. In this paper, we present the findings of a comprehensive literature review that will assist researchers in getting a head start on developing new solutions. The most prevalent models were variants of the state-of-the-art DeepFM model.
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References
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)
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)
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)
Inoue, D., Matsumoto, S.: Predicting CTR of regional flyer images using CNN. In: 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI) (2022)
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)
Karpus, A., Raczyńska, 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)
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)
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)
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)
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)
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. 393–403 (2019)
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)
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), 7–9 (2017)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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. 436–447. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75765-6_35
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)
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)
Zhao, Z., Yang, S., Liu, G., Feng, D., Xu, K.: Fint: Field-aware interaction neural network for click-through rate prediction. ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)
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)
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)
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)
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Leszczełowska, P., Bollin, M., Grabski, M. (2023). Systematic Literature Review on Click Through Rate Prediction. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_51
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DOI: https://doi.org/10.1007/978-3-031-42941-5_51
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