Clickthrough Rate (CTR) Prediction Algorithm

A Clickthrough Rate (CTR) Prediction Algorithm is a prediction algorithm that can be implemented by a CTR prediction system to solve a CTR prediction task.

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• Example(s):
  • a Deep Learning-based CTR Prediction Algorithm, such as Deep Interest Network (DIN) by (Zhou, Zhu et al., 2018).
  • a Personalized CTR Prediction Algorithm.
  • …
• Counter-Example(s):
  • an Earnings per Click Predictive Modeling Algorithm (for eEPC prediction).
  • a Item Purchase Prediction Algorithm (for item purchase prediction).
  • a Item Rating Prediction Algorithm (for item rating prediction).
  • a Dwell Time Prediction Algorithm (for dwell time prediction).
• See: Item Purchase Prediction Algorithm.

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References

2018

• (Zhou, Zhu et al., 2018) ⇒ Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. (2018). “Deep Interest Network for Click-through Rate Prediction.” In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2018).
  • QUOTE: ... Click-through rate prediction is an essential task in industrial applications, such as online advertising. …

2017

• (Guo, Tang et al., 2017) ⇒ Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. (2017). “DeepFM: A Factorization-machine based Neural Network for CTR Prediction.” In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. ISBN:978-0-9992411-0-3
  • QUOTE: ... In this paper, a new deep neural network is proposed for CTR prediction. The most related domains are CTR prediction and deep learning in recommender system. In this section, we discuss related work in these two domains.

     CTR prediction plays an important role in recommender system (Richardson et al., 2007; Juan et al., 2016; McMahan et al., 2013). Besides generalized linear models and FM, a few other models are proposed for CTR prediction, such as tree-based model (He et al., 2014), tensor based model (Rendle and Schmidt-Thieme, 2010), support vector machine (Chang et al., 2010), and bayesian model (Graepel et al., 2010). …

2016

• (Juan et al., 2016) ⇒ Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. (2016). “Field-aware Factorization Machines for CTR Prediction.” In: Proceedings of the 10th ACM Conference on Recommender Systems. ISBN:978-1-4503-4035-9 doi:10.1145/2959100.2959134
  • QUOTE: ... Click-through rate (CTR) prediction plays an important role in advertising industry [ Chappele et al., 2015, McMahan et al., 2013, Richardson et al., 2007 ]. Logistic regression is probably the most widely used model for this task (Richardson et al., 2007). …

2013

• (McMahan et al., 2013) ⇒ H. Brendan McMahan, Gary Holt, D. Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, and Jeremy Kubica. (2013). “Ad Click Prediction: A View from the Trenches.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2488200
  • QUOTE: Predicting ad click-through rates (CTR) is a massive-scale learning problem that is central to the multi-billion dollar online advertising industry. We present a selection of case studies and topics drawn from recent experiments in the setting of a deployed CTR prediction system.

2010

• (Sculley, 2010) ⇒ David Sculley. (2010). “Web-scale K-means Clustering.” In: Proceedings of the 19th International Conference on World Wide Web (WWW-2010).

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