Click Through Rate Prediction for Contextual Advertisment Using Linear Regression
This work addresses improving ad revenue for online advertisers, but it appears incremental as it applies a standard method to a known problem.
The paper tackles click-through rate prediction for contextual advertising using linear regression, reporting that the method fits data close to perfection with optimized feature selection.
This research presents an innovative and unique way of solving the advertisement prediction problem which is considered as a learning problem over the past several years. Online advertising is a multi-billion-dollar industry and is growing every year with a rapid pace. The goal of this research is to enhance click through rate of the contextual advertisements using Linear Regression. In order to address this problem, a new technique propose in this paper to predict the CTR which will increase the overall revenue of the system by serving the advertisements more suitable to the viewers with the help of feature extraction and displaying the advertisements based on context of the publishers. The important steps include the data collection, feature extraction, CTR prediction and advertisement serving. The statistical results obtained from the dynamically used technique show an efficient outcome by fitting the data close to perfection for the LR technique using optimized feature selection.