Ad-centric Model Discovery for Prediciting Ads's Click-through Rate

AbstractClick here and insert your abstract text. Search engine advertising has become one of the most important revenue models of electronic commerce. It strongly affects the probability that users click on the ads at the side of the search results page if the system shows the right ones. To maximize the outcome of search engine revenue and improve perception on those ads, it is important to understand the factors which affect the click through rate (CTR) on those ads. Tencent founded in 1998, is one of China's largest and most used Internet service portals. It provides a number of online services such as value-added Internet, mobile and telecom services and online advertising. As of September 30, 2011, Tencent had 711.7 million active Instant Messenger users. It forms the largest Internet Community in China. In this research, we use a very large dataset of Tencent click logs (soso.com) with millions records. First we describe how soso.com searching engine advertising works, our system architecture is designed with the click log dataset, and observations inside it aims at those ads with enough historical click logs. Then we show how to use ad-centric features to discover models that can find factors affecting CTR prediction performance. The proposed framework could help both optimizing the search engine system for soso.com and improving the ads designs for the advertisers