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Exploring an opinion network for taste prediction: An empirical study

Listed author(s):
  • Blattner, Marcel
  • Zhang, Yi-Cheng
  • Maslov, Sergei
Registered author(s):

    We develop a simple statistical method to find affinity relations in a large opinion network which is represented by a very sparse matrix. These relations allow us to predict missing matrix elements. We test our method on the Eachmovie data of thousands of movies and viewers. We found that significant prediction precision can be achieved and it is rather stable. There is an intrinsic limit to further improve the prediction precision by collecting more data, implying perfect prediction can never obtain via statistical means.

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    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 373 (2007)
    Issue (Month): C ()
    Pages: 753-758

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    Handle: RePEc:eee:phsmap:v:373:y:2007:i:c:p:753-758
    DOI: 10.1016/j.physa.2006.04.121
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    1. Paul Resnick & Neophytos Iacovou & Mitesh Suchak & Peter Bergstrom & John Riedl, 1994. "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Working Paper Series 165, MIT Center for Coordination Science.
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