Web Page Access Prediction based on Integrated Approach

Predicting the user's web page access is a challenging task that is continuing to gain importance as the web. Understanding users' next page access helps in formulating guidelines for web site personalization. Server side log files provide information that enables to reconstruct the user navigation sessions within the web site, where a session consists of a sequence of web pages viewed by a user within a given time. A web navigation behavior is helpful in understanding what information of online users demand. In this paper, we present the system that focuses on the improvements of predicting web page access. We proposed to use clustering techniques to cluster the web log data sets. As a result, a more accurate Markov model is built based on each group rather than the whole data sets. Markov models are commonly used in the identification of the next page to be accessed by the user based on the previously accessed pages. Then, we use popularity and similarity based-page rank algorithm to make prediction when the ambiguous results are found. Page Rank is a numeric value that represents how important a page is on the web. When one page links to another page, it is effectively casting a vote for the other page. The more votes for a page, the more important the page must be.