Title :
Does Wikipedia Information Help Netflix Predictions?
Author :
Lees-Miller, John ; Anderson, Fraser ; Hoehn, Bret ; Greiner, Russell
Author_Institution :
Dept. of Comput. Sci., Univ. of Alberta, AB
Abstract :
We explore several ways to estimate movie similarity from the free encyclopedia Wikipedia with the goal of improving our predictions for the Netflix Prize. Our system first uses the content and hyperlink structure of Wikipedia articles to identify similarities between movies. We then predict a user´s unknown ratings by using these similarities in conjunction with the user´s known ratings to initialize matrix factorization and k-Nearest Neighbours algorithms. We blend these results with existing ratings-based predictors. Finally, we discuss our empirical results, which suggest that external Wikipedia data does not significantly improve the overall prediction accuracy.
Keywords :
information networks; matrix algebra; search engines; Netflix Prize; Wikipedia articles; free encyclopedia; hyperlink structure; k-nearest neighbours algorithms; matrix factorization; Accuracy; Collaboration; Encyclopedias; Information filtering; Information filters; Internet; Motion pictures; Probes; Voting; Wikipedia; collaborative filtering; hybrid; netflix; recommender system; wikipedia;
Conference_Titel :
Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-0-7695-3495-4
DOI :
10.1109/ICMLA.2008.121
