Web page prefetching using temporal link analysis

Web searching should be as simple as providing the search engine with a query and having the search engine return a link to the desired Web page. Unfortunately, current Web search engines use text based queries and therefore require the user to provide keywords. Converting the users information need into a few key words is not a simple process. Due to this, Web search patterns involve the user visiting many Web pages that do not satisfy their information need, while interleaving this process with several visits to the Web search engine. Rather than the user actively searching for pages using key words, the search engine could provide pages to the user based on their Web usage patterns. By examining the users Web history, we will be able to compute the types of Web pages the user desires and hence provide search results to the user without the need for key words. This method of passive Web searching is called prefetching. Given that Google has had such success in using link analysis to provide useful retrieval results, we aim to investigate the utility of Web links to perform prefetching.

To date, we have successfully applied eigenvalue analysis to our temporal link graph (shown in Fig. 3) to obtain a measure of importance for each page. We will be examining the effect of using Hidden Markov models and their benefit over the eigenvalue analysis.