Research of the Collaborative Filtering Algorithm for E-Commerce

Ya Luo

doi:10.4028/www.scientific.net/AMR.121-122.717

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 121-122Research of the Collaborative Filtering Algorithm...

Research of the Collaborative Filtering Algorithm for E-Commerce

Abstract:

In this paper we mainly discuss the Collaborative Filtering algorithm which is widely applied in E-commerce. This article presents the idea of Collaborative Filtering algorithm and its main step. It analyzes the problem of cold start and data sparsity which affect the accuracy of prediction and results, as well as an idea of improvement Collaborative Filtering algorithm.

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Periodical:

Advanced Materials Research (Volumes 121-122)

Pages:

717-721

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.121-122.717

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Online since:

June 2010

Authors:

Ya Luo

Keywords:

Cold Star, Collaborative Filtering (CF), Data Sparsity, Personalization Recommendation

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References

[1] Deng Ailin, Zuo Ziye, Zhu Yangyong, Collaborative Filtering Recommendation Algorithm Based on Item Clustering, [J], Journal of Chinese Computer Systems, 2004, 25(9): 1665-1670.

Google Scholar

[2] Tao Hongliang, A Collaborative Filtering Recommendation Algorithm Based on Iterative Bidirectional Clustering, [D], Nanchang: Jiangxi Normal University, (2007).

Google Scholar

[3] Chen Donglin, Nie Guihua, Research on Collaborative Filtering Algorithm Based on Item's Attribute Implicit Rating, [J], Computer Application, 2006, 26(4): 966-968.

Google Scholar

[4] Li Xingxing, Research on E-commerce Recommendation System, [J], Computer Engineering and Science, 2004, 26(5): 7-10.

Google Scholar

[5] Deng Ailing, Zhu Yangyong , Shi Bole, A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction, [J], Journal of Software, 2003, (09): 1-8.

Google Scholar

[6] Wang Weiping, Wu Lun. Application of Collaborative Filtering in CRM-Based Cross-Selling [J]. Journal of Management, 2007, 4(4): 436-441.

Google Scholar

[7] GurRong Xue, HuajJun Zeng. Optimizing Web Search Using Web Click-through Data.

Google Scholar

[8] Wang Lang, Zhai Zhengju. Collaborative Filtering Algorithms Based on Time Weight [J]. Computer Application, 2007, 27(9): 2302.

Google Scholar

[9] Zhou Junfeng, Tang Xian, Guo Jinfeng, An Optimized Collaborative Filtering Recommendation Algorithm, [J], Computer Research and Development, 2004, (10): 1842-1847.

Google Scholar

[10] Yu Li, Liu Lu, Luo Zhanghua, Comparison and Analysis on E-Commence Recommendation Method in China, [J], System Engineering Theory and Practice, 2004, (08): 96-101.

Google Scholar