Click Through Rate Prediction Employing Wavelet Tree and Regression Learning
Keywords:
Online Advertising, Data Mining, Click Through Rates (CTR), Wavelet Tree, Regression Learning, Support Vector Regression, Prediction AccuracyAbstract
Click through rates have proven to be a critical factor in deciding the effectiveness of online advertising models. 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. Forecasting 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 forecasting problem, and thus can be estimated by machine learning models. The training data is collected from historical ads impressions and the corresponding clicks. An estimate of click through prior to fetching an add for a query is important for the accurate decision in the context. In this work a recursive binary partitioning algorithm is used along with support vector regression to estimate the bipolar nature of add clicks. A comparative analysis has also been made with exiting baseline techniques and it has been found that the proposed approach attains better performance metrics compared to baseline techniques.
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