An Improved Content-based Recommendation Method Through Collaborative Predictions and Fuzzy Similarity Measures
Jiang Shuhao1, Xue Fuliang2
1. Information Engineering College, Tianjin University of Commerce, Tianjin 300134, China;
2. Business School, Tianjin University of Finance & Economics, Tianjin 300222, China
[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.
姜书浩, 薛福亮. 一种利用协同过滤预测和模糊相似性改进的基于内容的推荐方法[J]. 现代图书情报技术, 2014, 30(2): 41-47.
Jiang Shuhao, Xue Fuliang. An Improved Content-based Recommendation Method Through Collaborative Predictions and Fuzzy Similarity Measures. New Technology of Library and Information Service, 2014, 30(2): 41-47.
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