Difference between revisions of "Using Wikipedia to Boost Collaborative Filtering Techniques"
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| − | '''Using Wikipedia to Boost Collaborative Filtering Techniques''' - scientific work related to Wikipedia quality published in 2011, written by Gilad Katz, Bracha Shapira, Lior Rokach and Guy Shani. | + | '''Using Wikipedia to Boost Collaborative Filtering Techniques''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Gilad Katz]], [[Bracha Shapira]], [[Lior Rokach]] and [[Guy Shani]]. |
== Overview == | == Overview == | ||
| − | 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. Authors overcome this problem by using the publicly available user generated information contained in Wikipedia. Authors 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. Authors 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. | + | 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. Authors overcome this problem by using the publicly available user generated information contained in [[Wikipedia]]. Authors 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. Authors 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. |
Revision as of 09:38, 23 October 2020
Using Wikipedia to Boost Collaborative Filtering Techniques - scientific work related to Wikipedia quality published in 2011, written by Gilad Katz, Bracha Shapira, Lior Rokach and Guy Shani.
Overview
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. Authors overcome this problem by using the publicly available user generated information contained in Wikipedia. Authors 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. Authors 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.
