Exploiting the web of data in model-based recommender systems
Proceedings of the sixth ACM conference on Recommender systems
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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.