Clickthrough Rate (CTR) Predictive Modeling Task

A Clickthrough Rate (CTR) Predictive Modeling Task is a predictive modeling task that produces clickthrough estimates (for clickthrough rate).

• Context:
  • Input: Clickthrough Data / Click-Through Rate (CTR) Estimation Dataset.
  • output: Clickthrough Rate Prediction Model (that makes clickthrough rate estimates such as clickthrough rate scores).
  • performance measure: CTR Prediction Quality Measure, such as per impression weighted AUC.
  • It can range from being a Personalized CTR Predictive Modeling Task to being a Service-Wide CTR Predictive Modeling Task.
  • It can be solved by a CTR Prediction System (that implements a CTR prediction algorithm).
  • …
• Example(s):
  • a Personalized CTR Prediction Task.
  • a Advertising CTR Prediction Task, such as a SERP Ad CTR Prediction Task.
  • …
• Counter-Example(s):
  • an Earnings per Click Predictive Modeling Task (for an eEPC).
  • a Item Purchase Prediction Task.
  • a Item Rating Prediction Task (for item ratings).
  • a Dwell Time Prediction Task.
• See: User-Item Interaction Prediction.

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References

2023

• (Wang et al., 2023) ⇒ Dong Wang, Kavé Salamatian, Yunqing Xia, Weiwei Deng, and Qi Zhang. (2023). “BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction.” In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2023).

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.

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