Exploiting Community Behavior for Enhanced Link Analysis and Web Search

Abstract

Methods for Web link analysis and authority ranking such as PageRank are based on the assumption that a user endorses a Web page when creating a hyperlink to this page. There is a wealth of additional user-behavior information that could be considered for improving authority analysis, for example, the history of queries that a user community posed to a search engine over an extended time period, or observations about which query-result pages were clicked on and which ones were not clicked on after a user saw the summary snippets of the top-10 results.

We study enhancements of link analysis methods by incorporating additional user assessments based on query logs and click streams, including negative feedback when a query-result page does not satisfy the user demand or is even perceived as spam. Our methods use various novel forms of Markov models whose states correspond to users and queries in addition to Web pages and whose links also reflect the relationships derived from query-result clicks, query refinements, and explicit ratings.