基于Widrow-Hoff神经网络的多指标推荐算法

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Abstract To solve the problem that the traditional collaborative filtering recommendation algorithm can not recommend with multiple criteria, a multi-criteria recommendation algorithm based on Widrow-Hoff neural network is proposed by introducing the concept of multi-criteria rating for extending the standard collaborative filtering algorithm. The Widrow-Hoff least mean square (LMS) adaptive algorithm has the characteristics of high accuracy fitting in the process of system identification. Based on that, an approach to compute user preferences eigenvector based on Widrow-Hoff LMS algorithm is proposed. The user preferences eigenvector and spatial distance are adopted to measure user similarity and then a neighbor set for the best recommendations is located. Experimental results show that the proposed algorithm improves the accuracy and the quality of recommendation.

Key words： Widrow-Hoff Neural Network Recommendation Algorithm Multiple Criteria Rating Similarity User Preference Eigenvector

Received: 27 January 2010

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	Articles by authors
	ZHANG Fu-Zhi
	CHANG Jun-Feng
	WANG Dong

Cite this article:

ZHANG Fu-Zhi,CHANG Jun-Feng,WANG Dong. Multi-Criteria Recommendation Algorithm Based on Widrow-Hoff Neural Network[J]. , 2011, 24(2): 233-242.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2011/V24/I2/233

[1] Adomavicius G, Tuzhilin A. Toward the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans on Knowledge and Data Engineering, 2005, 17(6): 734-749
[2] Teng Weiguang, Lee H H. Collaborative Recommendation with Multi-Criteria Ratings. Journal of Computers. 2007, 17(4): 69-78
[3] Adomavicius G, Kwon Y O. New Recommendation Techniques for Multicriteria Rating Systems. IEEE Intelligent Systems, 2007, 22(3): 48-55
[4] Lakiotaki K, Tsafarakis S, Matsatsinis N. UTA-Rec: A Recommender System Based on Multiple Criteria Analysis // Proc of the ACM Conference on Recommender Systems. Lausanne, Switzerland, 2008: 219-226
[5] Huang Zan, Chen H, Zeng D. Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering. ACM Trans on Information System, 2004, 22(1): 116-142
[6] Ge Lei, Huo Aiqing. Application Research of Widrow-Hoff Neural Network Learning Rule. Electronic Design Engineering, 2009, 17(6): 15-16,19 (in Chinese)
(葛蕾,霍爱清.Widrow-Hoff神经网络学习规则的应用研究.电子设计工程, 2009, 17(6): 15-16,19)
[7] Zhang Guangwei, Li Deyi, Li Peng, et al. A Collaborative Filtering Recommendation Algorithm Based on Cloud Model. Journal of Software, 2007, 18(10): 2403-2411 (in Chinese)
(张光卫,李德毅,李鹏,等.基于云模型的协同过滤推荐算法.软件学报, 2007, 18(10): 2403-2411)
[8] Deng Ailin, Zhu Yangyong, Shi Bole. A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction. Journal of Software, 2003, 14(9): 1621-1628 (in Chinese)
(邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法.软件学报, 2003, 14(9): 1621-1628)
[9] Sarwar B, Karypis G, Konstan J, et al. Item-Based Collaborative Filtering Recommendation Algorithms // Proc of the 10th International Conference on World Wide Web. Hong Kong, China, 2001: 285-295
[10] Zhang Binqi. A Collaborative Filtering Recommendation Algorithm Based on Domain Knowledge. Computer Engineering. 2005, 31(21): 79-85 (in Chinese)
(张丙奇.基于领域知识的个性化推荐算法研究.计算机工程, 2005, 31(21): 79-85)
[11] Xu Hailing, Wu Xiao, Li Xiaodong, et al. Comparison Study of Internet Recommendation System. Journal of Software, 2009, 20(2): 350-362 (in Chinese)
(许海玲,吴潇,李晓东,等.互联网推荐系统比较研究.软件学报, 2009, 20(2): 350-362)
[12] Ma Hongwei, Zhang Guangwei, Li Peng. Survey of Collaborative Filtering Algorithms. Journal of Chinese Computer Systems, 2009, 30(7): 1282-1288 (in Chinese)
(马宏伟,张光卫,李鹏.协同过滤推荐算法综述.小型微型计算机系统, 2009, 30(7): 1282-1288)
[13] Goldbreg K, Roeder T, Gupta D, et al. Eigentaste a Constant Time Collaborative Filtering Algorithm. Information Retrieval, 2001, 4(2): 133-151
[14] Silvestri F, Baragla R, Palmerini P, et al. On-Line Generation of Suggestions for Web Users // Proc of the International Conference on Information Technology: Coding Computing. Las Vegas, USA, 2004, Ⅰ: 392-397
[15] Zhang Lei, Chen Junliang, Meng Xiangwu, et al. BP Neural Networks-Based Collaborative Filtering Recommendation Algorithm. Journal of Beijing University of Posts and Telecommunications, 2009, 32(6): 42-46 (in Chinese)
(张磊,陈俊亮,孟祥武,等.基于BP神经网络的协作过滤推荐算法.北京邮电大学学报, 2009, 32(6): 42-46)