Pointing the way: active collaborative filtering
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
Knowledge infusion into content-based recommender systems
Proceedings of the third ACM conference on Recommender systems
A probabilistic approach to semantic collaborative filtering using world knowledge
Journal of Information Science
Pushing the boundaries of crowd-enabled databases with query-driven schema expansion
Proceedings of the VLDB Endowment
A live comparison of methods for personalized article recommendation at forbes.com
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Wiki3C: exploiting wikipedia for context-aware concept categorization
Proceedings of the sixth ACM international conference on Web search and data mining
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One important challenge in the field of recommender systems is the sparsity of available data. This problem limits the ability of recommender systems to provide accurate predictions of user ratings. We overcome this problem by using the publicly available user generated information contained in Wikipedia. We identify similarities between items by mapping them to Wikipedia pages and finding similarities in the text and commonalities in the links and categories of each page. These similarities can be used in the recommendation process and improve ranking predictions. We find that this method is most effective in cases where ratings are extremely sparse or nonexistent. Preliminary experimental results on the MovieLens dataset are encouraging.