GroupLens, WWW-Rating

[Thomas Roessler <roessler@guug.de>]

Unten angehängt ist
http://www.cs.umn.edu/Research/GroupLens/grouplens.html.

Wenn man den Gedanken weiterspinnt auf ein positives _wie_
_negatives_ Rating von WWW-Seiten, könnte man zu recht
interessanten Ergebnissen kommen: Die Kriterien des
Ratings sind letztlich in der Korrelation mit anderen
Ratenden verborgen und dadurch sehr viel differenzierter
als etwa ein rsaci-Rating.  Kultureller Hintergrund wird
prinzipbedingt in das Rating mit einbezogen, ebenso die
persönlichen Interessen.

Problematisch ist natürlich die benötigte Rechenpower -
und auch der liebe Datenschutz...

Schaut's Euch einfach mal an.

tlr
------------------------------

                      The GroupLens Recommendation System
_______________________________________________________________________________
                                       
   [1]How to Rate Articles
   
   [2]Register for the GroupLens Service
   
   [3]Download GroupLens Software
   
   [4]GroupLens Home The GroupLens System.
   
   The GroupLens system is an article recommendation system for
   electronic forums, specifically Usenet news. The purpose of GroupLens
   is to increase the value of time spent reading electronic forums.
   
   Internet newsgroups can carry hundreds of new postings every day. Many
   of these articles are off the newsgroup topic, and many more are not
   personally interesting to you. It is no longer feasible to read every
   article posted to a newsgroup in order to find interesting content.
   The GroupLens system makes reading Internet news productive again by
   highlighting articles of likely interest and warning against articles
   that will not be interesting. Using the GroupLens system, you won't
   miss the good articles and you won't waste your time on the bad ones.
   
   When you first see an article in a newsgroup, chances are that it has
   already been read by lots of other people. Each of these people formed
   an opinion of how interesting the content of that article is. Imagine
   you could talk to each of these people and could ask them for their
   opinions of the quality of the article. Based on these
   recommendations, you would determine if it was worth your time to read
   the article. Of course you can't talk to all those people, but the
   GroupLens system can, and all you have to do is tell GroupLens your
   opinion on articles you read. Users of the GroupLens service are asked
   to rate each article on a scale of 1 to 5, based on how interesting
   they found the article. The GroupLens system takes everybody's ratings
   and matches people who agree frequently. When you first see a new
   article, the recommendation system uses the ratings of people who
   agree with you to generate a prediction of your interest in the
   content of the article.
   
   Currently, the GroupLens system provides two free services to the
   Usenet community.
     * Personalized Predictions. As you rate articles, your ratings are
       compared with other users' ratings on the same articles. You are
       matched with people who repeatedly agree with you or repeatedly
       disagree with you. Ratings from these users are then use to
       generate a prediction reflecting your personalized interest.
       Personalized predictions are provided for a selected subset of
       [5]groups.
     * Average Predictions. Due to computational limitations, we can't
       provide personalized predictions to all newsgroups. We recognize
       that you may be excited about using the GroupLens system, but not
       read any of the supported groups. So for all other groups, we
       provide predictions that are not personalized but are the average
       rating. These are especially valuable in groups which contain many
       articles that everyone agrees are bad or good, such as groups with
       frequent flame wars or frequent spam attacks.
       
   Motivations
   
   The GroupLens service is part of the ongoing GroupLens research
   project at the University of Minnesota Department of Computer Science.
   It is being provided both as a public service and as a means to gather
   research data on personalized recommendation systems. We have no
   intention of charging money at any point in time. We are committed to
   providing it for 3 years, so you can depend on it being around for a
   while.
   
   Further Information
   
   [6]How to rate articles? (What should get a 5?)
   [7]How Does GroupLens Work?
   [8]Register for the GroupLens service
   [9]Download GroupLens-enhanced software
   [10]Original GroupLens announcement
   
       
    This page was last updated on: June 11th, 1997
       Questions or comments? [11]Please send us mail.

References

   1. http://www.cs.umn.edu/Research/GroupLens/rating.html
   2. http://www.cs.umn.edu/Research/GroupLens/registration.html
   3. http://www.cs.umn.edu/Research/GroupLens/software.html
   4. http://www.cs.umn.edu/Research/GroupLens/index.html
   5. http://www.cs.umn.edu/Research/GroupLens/supportedGroups.html
   6. http://www.cs.umn.edu/Research/GroupLens/rating.html
   7. http://www.cs.umn.edu/Research/GroupLens/devGLIntro.html
   8. http://www.cs.umn.edu/Research/GroupLens/registration.html
   9. http://www.cs.umn.edu/Research/GroupLens/software.html
  10. http://www.cs.umn.edu/Research/GroupLens/announcement.html
  11. mailto:grouplens-info@cs.umn.edu

-- 
Thomas Roessler · 74a353cc0b19 · dg1ktr · http://home.pages.de/~roessler/
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