使用机器学习方法的网页预测模型综述

P. A. Omosebi, A. P. Adewole, Oladipupo A. Sennaike
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引用次数: 0

摘要

网页分类和预取是减少网络访问延迟的关键技术。但是,由于服务器资源和网络带宽不足,当大多数预取的网页在后续访问中没有被访问时,会导致使用效率低下。为了解决这个问题,在预取期间进行准确的预测是至关重要的。现有的研究表明,使用马尔可夫模型等方法可以跟踪使用数据和用户导航,以预测最流行的网页路径,从而更准确地分类和预取网页。然而,由于网页内容不受控制的性质,以及需要对通过HTML标签和url链接的超文本进行分类,对网页进行准确分类仍然是一项具有挑战性的任务。本研究提出了几种方法来提高机器学习算法对网页分类的准确性。通过结合不同的结果,我们展示了我们的方法如何能够提高网页分类和预取的性能。我们的研究结果可以引导开发更高效、更准确的网页分类和预取算法,从而减少互联网访问延迟,这对互联网用户和提供商都具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Web Page Prediction Model using Machine Learning Approaches: A Review
Webpage classification and prefetching are essential techniques to reduce internet access latency. However, insufficient server resources and network bandwidth can lead to inefficient usage when the majority of the prefetched web pages are not visited during subsequent visits. To address this issue, accurate prediction during prefetching is crucial. Existing research has shown that usage data and user navigation can be tracked using methods like Markov models to predict the most popular web paths, leading to more accurate classification and prefetching of web pages. However, accurately classifying web pages remains a challenging task due to the uncontrolled nature of web content, and the need to classify hypertext that is linked through HTML tags and URLs. This study presents several methods to improve the accuracy of machine learning algorithms for classifying web pages. By combining different results, we show how our approach can enable increase in the performance of classification of web page and prefetching. Our findings can lead to the development of more efficient and accurate webpage classification and prefetching algorithms, resulting in reduced internet access latency, which is of great relevance to internet users and providers alike.
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