Dynamic Attention-Based Click-Through Rate Prediction Model
Authors: 
Zhiwei Ma, Ying DongAuthors Info & Claims
Zhiwei Ma, Ying DongAuthors Info & ClaimsArticle No.: 10, Pages 1 - 6
Abstract
Many online firms rely significantly on marketing and subscription rankings, such as Tencent and TikTok. CTR prediction is Critical in this area, but existing methods frequently underestimate the impact of feature interactions. To overcome this issue, a unique CTR model based on attention processes is presented. The goal of this approach is to represent the dynamic interactions between characteristics and their contextual importance. It does this through the employment of a global perception module, which defines overarching features, and a local perception module, which concentrates on finer-grained component dynamics. These perceived characteristics are then blended. Experiments on two publicly available datasets reveal that this strategy beats benchmarks designs, with a 0.65% AUC increase on the Avazu database and a 0.25% AUC gain on the Criteo dataset, confirming its efficacy in CTR prediction.
References
[1]
McMahan H B, Holt G, Sculley D, Ad click prediction: a view from the trenches[C]//Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013: 1222-1230
[2]
Juan Y, Zhuang Y, Chin W S, Field-aware factorization machines for CTR prediction[C]//Proceedings of the 10th ACM conference on recommender systems. 2016: 43-50
[3]
Juan Y, Lefortier D, Chapelle O. Field-aware factorization machines in a real-world online advertising system[C]//Proceedings of the 26th International Conference on World Wide Web Companion. 2017: 680-688
[4]
Graepel T, Candela J Q, Borchert T, Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft's bing search engine[C]. Omnipress, 2010
[5]
Moshayedi A J, Khan A S, Yang S, Personal image classifier based handy pipe defect recognizer (HPD): design and test[C]//2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2022: 1721-1728.
[6]
Zhou G, Mou N, Fan Y, Deep interest evolution network for click-through rate prediction[C]//Proceedings of the AAAI conference on artificial intelligence. 2019, 33(01): 5941-5948
[7]
Moshayedi A J, Roy A S, Taravet A, A secure traffic police remote sensing approach via a deep learning-based low-altitude vehicle speed detector through uavs in smart cites: Algorithm, implementation and evaluation[J]. Future Transportation, 2023, 3(1): 189-209.
[8]
Zhou G, Zhu X, Song C, Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018: 1059-1068
[9]
Huang T, Zhang Z, Zhang J. FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction[C]//Proceedings of the 13th ACM Conference on Recommender Systems. 2019: 169-177
[10]
Zhang W, Du T, Wang J. Deep Learning over Multi-field Categorical Data: –A Case Study on User Response Prediction[C]//Advances in Information Retrieval: 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20–23, 2016. Proceedings 38. Springer International Publishing, 2016: 45-57
[11]
LeCun Y, Bengio Y, Hinton G. Deep learning[J]. nature, 2015, 521(7553): 436-444
[12]
Cheng H T, Koc L, Harmsen J, Wide & deep learning for recommender systems[C]//Proceedings of the 1st workshop on deep learning for recommender systems. 2016: 7-10
[13]
Guo H, Tang R, Ye Y, DeepFM: a factorization-machine based neural network for CTR prediction[J]. arXiv preprint arXiv:1703.04247, 2017
[14]
Tian Z, Chen S, Li M, Dual-Modality Feature Extraction Network Based on Graph Attention for RGBT Tracking[C]//Proceedings of the 2022 3rd International Conference on Control, Robotics and Intelligent System. 2022: 248-253
[15]
Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141
[16]
Woo S, Park J, Lee J Y, Cbam: Convolutional block attention module[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 3-19
[17]
Wang Q, Wu B, Zhu P, ECA-Net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 11534-11542
[18]
Zhao Z, Yang S, Liu G, FINT: field-aware INTeraction neural network For CTR prediction[J]. arXiv preprint arXiv:2107.01999, 2021
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Published In
September 2023
133 pages
ISBN:9798400708312
DOI:10.1145/3625343
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 11 November 2023
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AIMLR 2023
AIMLR 2023: 2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics
September 15 - 17, 2023
Bangkok, Thailand
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