Discovering user access pattern based on probabilistic latent factor model

Guandong XU, Yanchun ZHANG, Jiangang MA, Xiaofang ZHOU

Research output: Chapter in Book/Report/Conference proceedingChapters

6 Citations (Scopus)

Abstract

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster. Copyright © 2005 Australian Computer Society, Inc.

Original languageEnglish
Title of host publicationProceedings of the 16th Australasian database conference, ADC 2005
Place of PublicationSydney
PublisherAustralian Computer Society
Pages27-35
Volume39
ISBN (Print)9781920682217
Publication statusPublished - 2005

Citation

Xu, G., Zhang, Y., Ma, J., & Zhou, X. (2005). Discovering user access pattern based on probabilistic latent factor model. In Proceedings of the 16th Australasian database conference, ADC 2005 (pp. 27-35). Australian Computer Society.

Keywords

  • Web usage mining
  • Web linkage information
  • User profile
  • Probabilistic latent semantic model

Fingerprint

Dive into the research topics of 'Discovering user access pattern based on probabilistic latent factor model'. Together they form a unique fingerprint.