| Title | CLR: Coupled Logistic Regression model for CTR prediction |
| Authors | Yin, Ning Li, Hongyan Su, Hanchen |
| Affiliation | Key Laboratory of Machine Perception, Peking University, Ministry of Education, Beijing, 100871, China School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China |
| Issue Date | 2017 |
| Publisher | 50th ACM Turing Conference - China, ACM TUR-C 2017 |
| Citation | 50th ACM Turing Conference - China, ACM TUR-C 2017. 2017, Part F127754. |
| Abstract | Online advertisement is a significant element of the Web browsing experience. A good advertising can not only bring benefits to publisher but also improve user satisfaction and extends advertiser's product marketing. To satisfy the desire of all three parties, the click through rate (CTR) prediction of a user to a specified ad in a specific context is of great importance. This challenging problem plays a key role in online advertising system and has to deal with several hard issues. Firstly, the model must process very high dimensional features from frequently changing ad, user and context, most of which are category features having large cardinality and sparse nature extending the dimensionality by two orders of magnitude. Secondly, nonlinear features such as conjunction information must be integrated into the model for a better prediction accuracy. Finally, the model must be able to parallelized efficiently to train from very large scale data sets. To address these problems, we proposed a novel model called Coupled Logistic Regression (CLR), for accurate and efficient CTR prediction. CLR can exploit all features from ad, user, context and nonlinear features among them by seamlessly integrate the conjunction information by employing factorization machine to achieve precise prediction result. And the high-dimensional problem is avoided by decomposing the decision function into two sub ones. Scalability of CLR is ensured through a newly invited MapReduce parallelization strategy, which can reduce communication and waiting time between nodes. Experimental results on real-world data set show that our CLR model can guarantee both accuracy and efficiency on large scale CTR prediction problems. ? 2017 ACM. |
| URI | http://hdl.handle.net/20.500.11897/504949 |
| ISSN | 9781450348737 |
| DOI | 10.1145/3063955.3063976 |
| Indexed | EI |
| Appears in Collections: | 机器感知与智能教育部重点实验室 信息科学技术学院 |
