Web Prefetching by ART1 Neural Network

摘要

As the Web becomes the major source for information and services, fast access to needed Web objects is a critical requirement for many applications. Various methods have been developed to achieve this goal. Web page prefetching is one of these methods that is commonly used and quite effective reducing the user perceived delays. In this paper, we proposed a new prefetching algorithm, called pARTl, based on the original ART1 algorithm that is a neural network approach for clustering. We modified the ART1 algorithm to obtain 2-way weights (bottom-up and top-down) between the clusters of hosts and the URLs (Web pages), and use these weights to make prefetching decisions. In the experiments we conducted, the new algorithm outperformed the original ART1 in terms of cache hit ratio.