Predicting web actions from HTML content
Proceedings of the thirteenth ACM conference on Hypertext and hypermedia
Emergent semantics and the multimedia semantic web
ACM SIGMOD Record
A Keyword-Based Semantic Prefetching Approach in Internet News Services
IEEE Transactions on Knowledge and Data Engineering
Popularity-Based Selective Markov Model
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Improving the performance of client web object retrieval
Journal of Systems and Software
Object prefetching using semantic links
ACM SIGMIS Database
Web prefetching performance metrics: a survey
Performance Evaluation
Semantic prefetching objects of slower web site pages
Journal of Systems and Software
WebAccel: Accelerating Web access for low-bandwidth hosts
Computer Networks: The International Journal of Computer and Telecommunications Networking
Web Page Prediction Based on Conditional Random Fields
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Referrer graph: a low-cost web prediction algorithm
Proceedings of the 2010 ACM Symposium on Applied Computing
Usefulness of local buffer data for WWW objects prefetching
International Journal of Intelligent Information and Database Systems
A comparison of prediction algorithms for prefetching in the current web
Journal of Web Engineering
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With the explosive growth of WWW applications on the Internet, users are experiencing access delays more often than ever. Recent studies showed that prefetching could alleviate the WWW latency largely than caching. Existing prefetching methods are mostly based on URL graphs. They use the graphical nature of hypertext links to determine the possible paths through a hypertext sys-tem. 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 prefetching 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 self-learning capability and good adaptivity to the change of user surfing interest. The technique was implemented in a SmartNews-Reader 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 prefetching. Of the prefetched documents, less than 30% was undesired.