Recommender System Based on User-generated Content

Recommender systems apply statistical and knowledge discovery techniques to the problem of making recommendations during live user interaction. This paper describes a novel approach of building recommender systems for the Web with the aid of usergenerated content. Recently certain communities of Internet users have engaged in creating high quality peer reviewed content for the Web. In our approach we are planning to extract the semantics of such user-generated content and to use these semantics to make more useful recommendations.

• Type: Conference
• Year: 2007
• Where: SYRCODIS
• Authors: Denis Turdakov
• Keywords: Building Recommender Systems, Peer Reviewed Content, Recommender Systems, SYRCODIS 2007, SYRCODIS 2008