Volume 8 Number 1 (Jan. 2013)
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JSW 2013 Vol.8(1): 176-183 ISSN: 1796-217X
doi: 10.4304/jsw.8.1.176-183

A Personalization Recommendation Algorithm for E-Commerce

Hui Li1, Shu Zhang2, Xia Wang1
1Department of Computer Engineering, Huai Hai institute of Technology, Lianyungang, China
2School of Business, Huai Hai institute of Technology, Lianyungang, China

Abstract—Many recommendation systems employ the collaborative filtering technology, which has been proved to be one of the most successful techniques in recommendation systems in recent years, the difficulties of the extreme sparsity of user rating data have become more and more severe. To solve the problems of scalability and sparsity in the collaborative filtering, this paper proposed a personalization recommendation algorithm based on rough set which is proposed, The algorithm refine the user ratings data with dimensionality reduction, then uses a new similarity measure to find the target users’ neighbors, then generates recommendations. To prove our algorithm’s effectiveness, the authors conduct experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.

Index Terms—E-commerce, recommendation, deduction, algorithms.


Cite: Hui Li, Shu Zhang, Xia Wang, "A Personalization Recommendation Algorithm for E-Commerce," Journal of Software vol. 8, no. 1, pp. 176-183, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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