Please wait a minute...
New Technology of Library and Information Service  2012, Vol. 28 Issue (3): 35-39    DOI: 10.11925/infotech.1003-3513.2012.03.06
Current Issue | Archive | Adv Search |
An Improved Collaborative Filtering Recommendation Algorithm Based on Vague Sets Theory
Zhang Huiying1, Xue Fuliang1,2
1. College of Management & Economics, Tianjin University, Tianjin 300072, China;
2. Business School, Tianjin University of Finance & Economics, Tianjin 300222, China
Download: PDF(626 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  Aiming at the difficulty of project features expression,this paper brings forward to extract and represent it with vague sets theory.Then similar item is clustered to predict missing evaluation values of item, thus eliminating the sparsity problem of collaborative filtering recommendation. Based on the predicted rating matrix,similar users are clustered,and collaborative filtering recommendation is implemented in the space of item cluster to give more targeted recommendation. Evaluation results show that the proposed method is more effective both in the accuracy and in relevance of recommendations.
Key wordsRecommender system      Collaborative filtering      Item similarity      Content-based recommendation      Vague sets     
Received: 06 January 2012      Published: 19 April 2012



Cite this article:

Zhang Huiying, Xue Fuliang. An Improved Collaborative Filtering Recommendation Algorithm Based on Vague Sets Theory. New Technology of Library and Information Service, 2012, 28(3): 35-39.

URL:     OR

[1] 许海玲,吴潇,李晓东,等.互联网推荐系统比较研究[J]. 软件学报,2009,20(2):350-362.(Xu Hailing, Wu Xiao, Li Xiaodong, et al. Comparison Study of Internet Recommendation System[J].Journal of Software, 2009,20(2):350-362.)

[2] Pazzani M J.A Framework for Collaborative, Content-based and Demographic Filtering[J]. Artificial Intelligence Review, 1999,13(5):393-408.

[3] 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. 2000.

[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] Devi M K, Venkatesh P.Kernel Based Collaborative Recommender System for E-purchasing[J]. Academy of Sciences,2010,35(5):513-524.

[6] Zenebe A,Norcio A F.Representation,Similarity Measures and Aggregation Methods Using Fuzzy Sets for Content-based Recommender Systems[J].Fuzzy Sets and Systems,2009,160(1):76-94.

[7] 崔春生,李光, 吴祈宗. 基于Vague 集的电子商务推荐系统研究[J]. 计算机工程与应用,2011,47(10):237-239.(Cui Chunsheng,Li Guang, Wu Qizong. Research on Recommender Systems of Electric Commerce Based on Vague Sets[J]. Computer Engineering and Applications,2011,47(10):237-239.)

[8] 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.)

[9] Kim K J, Ahn H. A Recommender System Using GA K-means Clustering in an Online Shopping Market[J]. Expert Systems with Applications, 2008,34(2):1200-1209.

[10] 牟向伟,陈燕.基于模糊描述逻辑的个性化推荐系统建模[J]. 计算机应用研究,2011,28(4):1430-1433.(Mu Xiangwei,Chen Yan. Fuzzy Semantic Personalized Recommendation System Modeling[J]. Application Research of Computers,2011,28(4):1430-1433.)
[1] Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[2] Daoping Wang,Zhongyang Jiang,Boqing Zhang. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
[3] Yong Wang,Yongdong Wang,Huifang Guo,Yumin Zhou. Measuring Item Similarity Based on Increment of Diversity[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
[4] Lingfeng Hua,Gaoming Yang,Xiujun Wang. Recommending Diversified News Based on User’s Locations[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[5] Fuliang Xue,Junling Liu. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[6] Xingxin Qin,Rongbo Wang,Xiaoxi Huang,Zhiqun Chen. Slope One Collaborative Filtering Algorithm Based on Multi-Weights[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
[7] Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time[J]. 现代图书情报技术, 2016, 32(9): 65-69.
[8] Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
[9] Wang Yong,Deng Jiangzhou,Deng Yongheng,Zhang Pu. A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution[J]. 现代图书情报技术, 2016, 32(6): 73-79.
[10] Ma Li. Collaborative Filtering Recommendation Method Based on User Learning Tree[J]. 现代图书情报技术, 2016, 32(4): 72-80.
[11] Shuhao Jiang, Liyi Zhang, Zhixin Zhang. New Collaborative Filtering Algorithm Based on Relative Similarity[J]. 数据分析与知识发现, 2016, 32(12): 44-49.
[12] Wu Yingliang, Yao Huaidong, Li Cheng'an. An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship[J]. 现代图书情报技术, 2015, 31(9): 38-45.
[13] Zhu Ting, Qin Chunxiu, Li Zuhai. Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification[J]. 现代图书情报技术, 2015, 31(6): 13-19.
[14] Gao Huming, Zhao Fengyue. A Hybrid Recommendation Method Combining Collaborative Filtering and Content Filtering[J]. 现代图书情报技术, 2015, 31(6): 20-26.
[15] Ying Yan, Cao Yan, Mu Xiangwei. A Hybrid Collaborative Filtering Recommender Based on Item Rating Prediction[J]. 现代图书情报技术, 2015, 31(6): 27-32.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938