Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models

This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the total number of visits of a given Web page by all incoming users at the same time. The prediction technique can be used as a component for many Web based applications . The prediction model regards a Web browsing session as a continuous-time Markov process where the transition probability matrix can be computed from Web log data using the Kolmogorov’s backward equations. The model is tested against real Web-log data where the scalability and accuracy of our method are analyzed.

• Type: Conference
• Year: 2004
• Where: PAKDD
• Authors: Qiming Huang, Qiang Yang, Joshua Zhexue Huang, Michael K. Ng
• Keywords: Data Mining, PAKDD 2004, Prediction Model, Web Browsing Session, Web Page