Neural Nets based Predictive Prefetching to Tolerate WWW Latency

With the explosive growth of WWW applications on the Internet, users are experiencing access delays more often than ever. Recent studies showed that pre-fetching could alleviate the WWW latency to a larger extent than caching. Existing pre-fetching methods are mostly based on URL graphs. They use the graphical nature of hypertext links to determine the possible paths through a hypertext system. While they have been demonstrated effective in pre-fetching of documents that are often accessed, they are incapable of pre-retrieving documents whose URLs had never been accessed. In this paper, we propose a context-specific pre-fetching technique to overcome the limitation. It relies on keywords in anchor texts of URLs to characterize user access patterns and on neural networks over the keyword set to predict future requests. It features a selflearning capability and good adaptivity to the change of user surfing interest. The technique was implemented in a SmartNewsReader system and cross-examined in a daily browsing of MSNBC and CNN news sites. The experimental results showed an achievement of approximately 60% hit ratio due to pre-fetching. Of the pre-fetched documents, less than 30% was undesired.