Exploring an opinion network for taste prediction: An empirical study
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.
Volume (Year): 373 (2007)
Issue (Month): C ()
|Contact details of provider:|| Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:373:y:2007:i:c:p:753-758. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamier, Wendy)
If references are entirely missing, you can add them using this form.