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New Technology of Library and Information Service  2014, Vol. 30 Issue (7): 56-63    DOI: 10.11925/infotech.1003-3513.2014.07.08
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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
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[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.

Key wordsRecommender system      Collaborative filtering      SOM      Radial basis function     
Received: 13 February 2014      Published: 20 October 2014
:  TP301.6  

Cite this article:

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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[1] Sarwar B M, Karypis G, Konstan J A, et al. Application of Dimensionality Reduction in Recommender Systems: A Case Study[C]. In: Proceedings of ACM WebKDD Workshop. New York: ACM, 1998.
[2] Schafer J B, Konstan J, Riedl J. Recommender Systems in E-commerce[C]. In: Proceedings of the 1st ACM Conference on Electronic Commerce. New York, NY, USA: ACM, 1999: 158-166.
[3] Pazzani M J. A Framework for Collaborative, Content-Based, and Demographic Filtering[J]. Artificial Intelligence Review, 1999, 13(5-6): 393-408.
[4] Cheung K W, Kwok J T, Law M H,et al. Mining Customer Product Ratings for Personalized Marketing[J]. Decision Support Systems, 2003, 35(2): 231-243.
[5] 李晓昀, 阳小华, 余颖. 基于隐性反馈分析的个性化推荐研究[J]. 计算机工程与设计, 2009, 30(16): 3794-3796, 3825. (Li Xiaoyun, Yang Xiaohua, Yu Ying. Research on Individuaized Recommendation Based on Implicit FeedbackAnalyses[J]. Computer Engineering and Design, 2009, 30(16): 3794-3796, 3825.)
[6] Shih Y Y, Liu D R. Product Recommendation Approaches: Collaborative Filtering via Customer Lifetime Value and Customer Demands[J]. Expert Systems with Applications, 2005, 35 (1-2): 350-360.
[7] 邓爱林. 电子商务推荐系统关键技术研究[D]. 上海: 复旦大学, 2003. (Deng Ailin. The Research on Key Technologies of Recommendation System in E-Commerce[D]. Shanghai: Fudan University, 2003.)
[8] 陈逸, 于洪. 一种基于相同评分矩阵的协同过滤补值算法[J]. 计算机应用研究, 2009, 26(12): 4513-4515, 4519. (Chen Yi, Yu Hong. Collaborative Filtering Filling Miss Values Algorithm Based on Co-Rating Matrix[J]. Application Research of Computers, 2009, 26(12): 4513-4515, 4519.)
[9] 张慧颖, 薛福亮. 一种集成客户终身价值与协同过滤的推荐方法[J]. 现代图书情报技术, 2012(1): 46-52. (Zhang Huiying, Xue Fuliang. An Integrated Recommender Method Based on CLV and Collaborative Filtering[J]. New Technology of Library and Information Service, 2012(1): 46-52.)
[10] Xue G R, Lin C X, Yang Q, et al. Scalable Collaborative Filtering Using Cluster-based Smoothing[C]. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil. ACM, 2005.
[11] 肖强, 钱晓东, 武振锋. 一种改进的SOM神经网络对Web用户的聚类[J]. 情报科学, 2012, 30(6): 820-824. (Xiao Qiang, Qian Xiaodong, Wu Zhenfeng. Improved SOM Neural Network to Clustering Web Users[J]. Information Science, 2012, 30(6): 820-824.)
[12] Kavitha Devi M K, Venkatesh P. Kernel Based Collaborative Recommender System for E-Purchasing[J]. Academy of Sciences, 2011, 35(5): 513-524.
[13] 严冬梅, 鲁城华. 基于用户兴趣度和特征的优化协同过滤推荐[J]. 计算机应用研究, 2012, 29(2): 497-500. (Yan Dongmei, Lu Chenghua. Optimized Collaborative Filtering Recommendation Based on User’ Interest Degree and Feature[J]. Application Research of Computers, 2012, 29(2): 497-500.)
[14] 熊忠阳, 刘芹, 张玉芳, 等. 基于项目分类的协同过滤改进算法[J]. 计算机应用研究, 2012, 29(2): 493-496. (Xiong Zhongyang, Liu Qin, Zhang Yufang, et al. Improved Algorithm of Collaborative Filtering Based on Item Classification[J]. Application Research of Computers, 2012, 29(2): 493-496.)

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