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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.
This paper enhances 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 advanced 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. Preliminary experiments are presented as a
proof of concept.