Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user. There has been considerable work on user browsing models, to model and analyze both the examination and the relevance components of CTR. In this paper, we propose a novel formulation: a micro-browsing model for how users read result snippets. The snippet text of a result often plays a critical role in the perceived relevance of the result. We study how particular words within a line of snippet can influence user behavior. We validate this new micro-browsing user model by considering the problem of predicting which snippet will yield higher CTR, and show that classification accuracy is dramatically higher with our micro-browsing user model. The key insight in this paper is that varying relatively few words within a snippet, and even their location within a snippet, can have a significant influence on the clickthrough of a snippet.
Coupled logistic regression (CLR) @cite_6 uses all features from ad, user, context and nonlinear features such as conjunction information. It integrates the conjunction information by employing factorization machine to achieve precise CTR prediction result.
@cite_6
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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.