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Research of User-Based Collaborative Filtering Algorithm in Tag Recommendation

Collaborative Filtering (CF) is one of the most popular techniques used in recommendation systems. User-based CF uses rating data to calculate similarity between users and is effective in traditional recommendation systems. However, it does not work well when applied in tag recommendation systems as there are tags with same semantics which does not match literally. Based on tag concept category, the improvement of traditional CF is achieved by compressing the dataset, analyzing the users preference and extracting the tags objective characteristics. The result of experiment shows the more satisfied accuracy than the traditional one does.