A Variable Granularity User Classification Algorithm Based on Multi-dimensional Features of Users

Author

Jia, Dawen ; Zeng, Cheng ; Peng, Zhiyong ; Cheng, Peng ; Yang, Zhimin

Author_Institution

State Key Lab. of Software Engneering, Wuhan Univ., Wuhan, China

fYear

2012

fDate

16-18 Nov. 2012

Firstpage

43

Lastpage

48

Abstract

Classifying Web users based on multi-dimensional features is one of the foundations of realizing personalized Web applications. It could be used for user classification model, users´ multi-dimensional data analysis, potential user group discovery and personalized recommendation and so forth. In this paper, a variable granularity user classification algorithm based on Web users´ multidimensional features is proposed. Given a user feature model, the algorithm will mine all common feature categories and find the relationships between them. A series of experiments are conducted to analyze the performances of this algorithm with different condition. The experimental results indicate that this algorithm has good performance and can be deployed in Web applications with massive Web users.

Keywords

Internet; data analysis; data mining; pattern classification; Web user classification; common feature category mining; multidimensional data analysis; multidimensional feature; personalized Web application; personalized recommendation; user group discovery; variable granularity user classification algorithm; Algorithm design and analysis; Classification algorithms; Communities; Data mining; Itemsets; Social network services; Variable granularity; feature subspace; hierarchy classification; user model;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Information Systems and Applications Conference (WISA), 2012 Ninth

Conference_Location

Haikou

Print_ISBN

978-1-4673-3054-1

Type

conf

DOI

10.1109/WISA.2012.45

Filename

6385181