@inproceedings{08176366fee24b9fafc6147c0bc7c744,
title = "Using Wikipedia to boost collaborative filtering techniques",
abstract = "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.",
keywords = "Wikipedia, cold start problem, collaborative filtering, recommender systems",
author = "Gilad Katz and Nir Ofek and Bracha Shapira and Lior Rokach and Guy Shani",
year = "2011",
month = oct,
day = "23",
doi = "10.1145/2043932.2043984",
language = "English",
isbn = "9781450306836",
series = "RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems",
publisher = "Association for Computing Machinery",
pages = "285--288",
booktitle = "RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems",
note = "5th ACM Conference on Recommender Systems, RecSys 2011 ; Conference date: 23-10-2011 Through 27-10-2011",
}