Using Wikipedia to boost collaborative filtering techniques

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

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.

Original languageEnglish
Title of host publicationRecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems
Pages285-288
Number of pages4
DOIs
StatePublished - 6 Dec 2011
Event5th ACM Conference on Recommender Systems, RecSys 2011 - Chicago, IL, United States
Duration: 23 Oct 201127 Oct 2011

Publication series

NameRecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems

Conference

Conference5th ACM Conference on Recommender Systems, RecSys 2011
Country/TerritoryUnited States
CityChicago, IL
Period23/10/1127/10/11

Keywords

  • Wikipedia
  • cold start problem
  • collaborative filtering
  • recommender systems

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