Tools and Techniques for Evaluating Web Information Retrieval Using Click-Through Data

Search has arguably become the dominant paradigm for finding information on the World Wide Web. In order to build a successful search engine, there are a number of challenges that arise where techniques from artificial intelligence can be used to have a significant impact. In this paper, we explore a number of problems related to finding information on the web and discuss approaches that have been employed in various research programs, including some of those at Google. Specifically, we examine issues of such as web graph analysis, statistical methods for inferring meaning in text, and the retrieval and analysis of newsgroup postings, images, and sounds. We show that leveraging the vast amounts of data on web, it is possible to successfully address problems in innovative ways that vastly improve on standard, but often data impoverished, methods. We also present a number of open research problems to help spur further research in these areas