A real-time evolutionary algorithm for Web prediction

Dario Bonino, Fulvio Corno, Giovanni Squillero
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引用次数: 34

Abstract

As an increasing number of users access information on the World Wide Web, there is a opportunity to improve well known strategies for Web caching, prefetching, dynamic user modeling and dynamic site customization in order to obtain better subjective performance and satisfaction in Web surfing. We propose a new method to exploit user navigational path behavior to predict, in real-time, future requests. Predicting user next requests is useful not only for document caching/prefetching, it is also suitable for quick dynamic portal adaptation to user behavior. Real-time user adaptation prevents the use of statistical techniques on Web logs, and we propose the adoption of a predictive user model based on finite state machines together with an evolutionary algorithm that evolves a population of FSMs for achieving a good prediction rate.
Web预测的实时进化算法
随着越来越多的用户在万维网上访问信息,有机会改进众所周知的Web缓存、预取、动态用户建模和动态站点自定义策略,以获得更好的主观性能和Web冲浪满意度。我们提出了一种利用用户导航路径行为来实时预测未来请求的新方法。预测用户的下一个请求不仅对文档缓存/预取有用,而且也适用于根据用户行为进行快速动态门户调整。实时用户适应阻止了在Web日志上使用统计技术,我们建议采用基于有限状态机的预测用户模型以及进化算法,该算法可以进化fsm种群以实现良好的预测率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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