Prediction of user navigation patterns by mining the temporal web usage evolution |
| |
Authors: | Vincent S Tseng Kawuu Weicheng Lin Jeng-Chuan Chang |
| |
Affiliation: | (1) Institute of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC |
| |
Abstract: | Advances in the data mining technologies have enabled the intelligent Web abilities in various applications by utilizing the
hidden user behavior patterns discovered from the Web logs. Intelligent methods for discovering and predicting user’s patterns
is important in supporting intelligent Web applications like personalized services. Although numerous studies have been done
on Web usage mining, few of them consider the temporal evolution characteristic in discovering web user’s patterns. In this paper, we propose
a novel data mining algorithm named Temporal N-Gram (TN-Gram) for constructing prediction models of Web user navigation by considering the temporality property in Web usage evolution.
Moreover, three kinds of new measures are proposed for evaluating the temporal evolution of navigation patterns under different
time periods. Through experimental evaluation on both of real-life and simulated datasets, the proposed TN-Gram model is shown to outperform other approaches like N-gram modeling in terms of prediction precision, in particular when the
web user’s navigating behavior changes significantly with temporal evolution. |
| |
Keywords: | Temporal patterns Navigation patterns Data mining Personalized services |
本文献已被 SpringerLink 等数据库收录! |
|