Personalized PageRank for Web Page Prediction Based on Access Time-Length and Frequency

Web page prefetching techniques are used to address the access latency problem of the Internet. To perform successful prefetching, we must be able to predict the next set of pages that will be accessed by users. The PageRank algorithm used by Google is able to compute the popularity of a set of Web pages based on their link structure. In this paper, a novel PageRank-like algorithm is proposed for conducting Web page prediction. Two biasing factors are adopted to personalize PageRank, so that it favors the pages that are more important to users. One factor is the length of time spent on visiting a page and the other is the frequency that a page was visited. The experiments conducted show that using these two factors simultaneously to bias PageRank results in more accurate Web page prediction than other methods that use only one of these two factors.