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Improved Collaborative Filtering Recommendation Based on Classification and User Trust 被引量:3
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作者 Xiao-Lin Xu Guang-Lin Xu 《Journal of Electronic Science and Technology》 CAS CSCD 2016年第1期25-31,共7页
When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes ... When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation. 展开更多
关键词 Collaborative filtering credibility of ratings evaluation on user trust item classification similarity metric
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