Campus Event Calendar
Event Entry
What and Who
Exploiting Community Behavior for Link Analysis
Julia Luxenburger
Max-Planck-Institut für Informatik - AG 5
Ringvorlesung
AG 1, AG 2, AG 3, AG 4, AG 5
MPI Audience
Note: We use this to send email in the morning.
Date, Time and Location
Thursday, 22 June 2006
13:00
-- Not specified --
E1 3 - Hörsaal Gebäude
16
Saarbrücken
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.
In my talk I present some current work on enhancing 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.
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.
In my talk I present some current work on enhancing 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.
Contact
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logged in users only
Veronika Weinand, 06/19/2006 12:57
Veronika Weinand, 04/26/2006 14:28
Veronika Weinand, 04/26/2006 14:21 -- Created document.
Veronika Weinand, 04/26/2006 14:28
Veronika Weinand, 04/26/2006 14:21 -- Created document.