A Review on Data Mining Models for Click-Through-Rate Prediction

During Online advertising is a multi-billion dollar industry that has served as one of the great success stories for machine earning. Sponsored search advertising, contextual advertising, display advertising, and real-time bidding auctions have all relied heavily on the ability of learned models to predict ad click–through rates accurately, quickly, and reliably. Predicting ad click–through rates (CTR) is a massive-scale learning problem that is central to the multi -billion dollar online advertising industry. Search engine advertising has become a significant element of the web browsing experience. Choosing the right ads for a query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. Accurately estimating the click-through rate (CTR) of ads has a vital impact on the revenue of search businesses; even a 0.1% accuracy improvement in production would yield hundreds of millions of dollars in additional earnings. An ad’s CTR is usually modelled as a classification problem, and thus can be estimated by machine learning models. The training data is collected from historical ads impressions and the corresponding clicks. A comprehensive review is presented in the paper pertaining to the supervised learning architecture for the prediction model.