Using speculation to reduce server load and service time on the WWW
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Using predictive prefetching to improve World Wide Web latency
ACM SIGCOMM Computer Communication Review
Predicting users' requests on the WWW
UM '99 Proceedings of the seventh international conference on User modeling
Predicting web actions from HTML content
Proceedings of the thirteenth ACM conference on Hypertext and hypermedia
Neural Nets Based Predictive Prefetching to Tolerate WWW Latency
ICDCS '00 Proceedings of the The 20th International Conference on Distributed Computing Systems ( ICDCS 2000)
A Data Mining Algorithm for Generalized Web Prefetching
IEEE Transactions on Knowledge and Data Engineering
A data cube model for prediction-based web prefetching
Journal of Intelligent Information Systems - Special issue on web intelligence
Delfos: the Oracle to Predict NextWeb User's Accesses
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
A comparison of prediction algorithms for prefetching in the current web
Journal of Web Engineering
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This paper presents the Referrer Graph (RG) web prediction algorithm as a low-cost solution to predict next web user accesses. RG is aimed at being used in a real web system with prefetching capabilities without degrading its performance. The algorithm learns from user accesses and builds a Markov model. These kinds kind of algorithms use the sequence of the user accesses to make predictions. Unlike previous Markov model based proposals, the RG algorithm differentiates dependencies in objects of the same page from objects of different pages by using the object URI and referrer in each request. This permits us to build a simple data structure that is easier to handle and, consequently, with a lower computational cost in comparison with other algorithms. The RG algorithm has been evaluated and compared with the best prediction algorithms proposed in the open literature, and the results show that it achieves similar precision values and page latency savings but requiring much less computational and memory resources.