%A Jiang Shuhao, Xue Fuliang %T An Improved Content-based Recommendation Method Through Collaborative Predictions and Fuzzy Similarity Measures %0 Journal Article %D 2014 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.1003-3513.2014.02.06 %P 41-47 %V 30 %N 2 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_3855.shtml} %8 2014-02-25 %X

[Objective] The authors improvecontent-based recommendation method through Fuzzy similarity-based collaborative filtering prediction and diversity selection algorithm to raise the recommendation quality. [Context] There are many successful applications of Content Based Recommender Systems (CB-RS).Recommendation diversity, representation of items as well as users' preference modeling are still critical parts in this field. [Methods] An effective collaborative Content-Based Filtering (CBF) is developed by introducing an item representation scheme, and measuring similarity based on the scheme, and fuzzy similarity measure and fuzzy-CF into the fuzzy-CBF with diversity, in order to improve content-based recommendation method. [Results] Experiment results show that the proposed hybrid scheme (fuzzy CF-CBF) is better than the other three popular schemes in Mean Absolute Error(MAE), coverage and diversity. [Conclusions] The proposed scheme improves the recommendation quality, while enhances the recommended diversity.