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

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.