Abstract. This paper presents a novel approach to successfully predict Web pages that are most likely to be re-accessed in a given period of time. We present the design of an intelligent predictor that can be implemented on a Web server to guide caching strategies. Our approach is adaptive and learns the changing access patterns of pages in a Web site. The core of our predictor is a neural network that uses a back-propagation learning rule. We present results of the application of this predictor on static data using log files; it can be extended to learn the distribution of live Web page access patterns. Our simulations show fast learning, uniformly good prediction, and up to 82% correct prediction for the following six months based on a oneday training data. This long-range prediction accuracy is attributed to the static structure of the test Web site.
A predictor that learns the patterns of Web pages and predicts the future accesses is presented in REF .
An Adaptive Web Cache Access Predictor Using Neural Network
In Proceedings of the15th International Conference on Industrial and Engineering. Applications of Artificial Intelligence and Expert Systems
Computer Science