User behavior modeling and content based speculative web page prefetching
This paper provides a transparent and speculative algorithm for content based web page prefetching. The algorithm relies on a profile based on the Internet browsing habits of the user. It aims at reducing the perceived latency when the user requests a document by clicking on a hyperlink. The proposed user profile relies on the frequency of occurrence for selected elements forming the web pages visited by the user. These frequencies are employed in a mechanism for the prediction of the users future actions. For the anticipation of an adjacent action, the anchored text around each of the outbound links is used and weights are assigned to these links. Some of the linked documents are then prefetched and stored in a local cache according to the assigned weights. The proposed algorithm was tested against three different prefetching algorithms and yield improved cachehit rates given a moderate bandwidth overhead. Furthermore, the precision of accurately inferring the users preference is evaluated through the recallprecision curves. Statistical evaluation testifies that the achieved recallprecision performance improvement is significant.
user behavior modeling link prefetching bigrams