Design and realization of personalized service in electronic commerce

Author

Xiao-liang, Liu

Author_Institution

Dept. of Bus., Hebei Univ. of Econ. & Bus., Shijiazhuang, China

fYear

2009

fDate

8-11 Aug. 2009

Firstpage

29

Lastpage

33

Abstract

Classical collaborative filtering recommendation is the most successful recommendation algorithm in electronic commerce system application. However, along with the continuous increase of site structure, content complexity and user number, data is extremely sparse and the real-time property and recommendation accuracy of algorithm decrease significantly, even no any commodity can be recommended. This paper classifies the users in electronic commerce by collaborative clustering and carries out different page recommendations for different types of users to realize the personalized service in electronic commerce.

Keywords

electronic commerce; information filtering; classical collaborative filtering recommendation; collaborative clustering; content complexity; electronic commerce system application; page recommendations; personalized service; site structure; user number; Algorithm design and analysis; Business; Clustering algorithms; Collaboration; Electronic commerce; Electronic mail; Filtering algorithms; Information filtering; Information filters; Uniform resource locators; collaborative clustering; electronic commerce; personalized service;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-4519-6

Electronic_ISBN

978-1-4244-4520-2

Type

conf

DOI

10.1109/ICCSIT.2009.5234605

Filename

5234605