A Research of Collaborative Filtering Recommender Method Based on SOM and RBFN Filling Missing Values
Xue Fuliang1, Zhang Huiying2
1. Business School, Tianjin University of Finance&Economics, Tianjin 300222, China;
2. College of Management&Economics, Tianjin University, Tianjin 300072, China
[Objective] To improve recommendation quality of collaborative filtering recommender method based on Self Organizing Map(SOM) and Radial Basis Function Neural Network (RBFN).[Context] Aiming at sparsity problems in collaborative filtering method, this paper proposes to predict missing evaluation values with artificial neural networks, and puts forward a new solutions to improve recommendation accuracy.[Methods] This paper puts forward pre-clustering similar users based on user rating matrix with SOM neural network. Based on the similarity of users in the same cluster, RBFN is used to fill missing values in sparse rating matrix. After that, collaborative filtering is used to generate recommendation based on complete rating matrix.[Results] Compared with traditional mainstreamfiltering method, MAE and F-Measure experimental results show that the proposed method is more effective both in theaccuracy and relevance of recommendations.[Limitations] The proposed method is only tested on the public data set from Movie Lens, and it need further examination in other data sets.[Conclusions] The recommender method proposed in this paper solves the sparsity problem in collaborative filtering recommendation to a certain extent, and it is also aguidance to solve the cold start and scalability problems.
薛福亮, 张慧颖. 一种基于自组织映射与径向基函数预测补值的协同过滤推荐方法[J]. 现代图书情报技术, 2014, 30(7): 56-63.
Xue Fuliang, Zhang Huiying. A Research of Collaborative Filtering Recommender Method Based on SOM and RBFN Filling Missing Values. New Technology of Library and Information Service, 2014, 30(7): 56-63.
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