一种基于多信息的改进鲨鱼搜索算法

Zhumin Chen, Jun Ma, Jingsheng Lei, Bo Yuan, Li Lian
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引用次数: 14

摘要

随着万维网的飞速发展,现有的通用搜索引擎已经呈现出更多的局限性。集中爬行越来越被视为一种潜在的解决方案。聚焦爬行的关键是如何准确预测已知url指向的未访问网页与给定主题的相关性。介绍了预测过程的形式化描述。然后,提出了四种策略来预测未访问页面与主题的相关性。进一步将这些策略的组合用于改进Shark-Search算法,Shark-Search是一种经典的基于网页文本信息的聚焦爬行算法。通过大量的实验来确定优化后的组合,验证了改进后的Shark-Search比原组合更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improved Shark-Search Algorithm Based on Multi-information
With the enormous growth of world wide web, existing general-purpose search engines have presented much more limitations. Focused crawling is increasingly seen as a potential solution. The key of focused crawling is how to accurately predict the relevance of the unvisited web pages pointed to by known URLs to a given topic. A formalized description of the predicting process is introduced. Then, four policies are proposed to predict the relevance of unvisited pages to a topic. Further the combinations of these policies are used to improve the Shark-Search, which is a classic focused crawling algorithm mainly based on the textual information of Web pages. A large number of experiments were carried out to identify the optimized combination and verify that the improved Shark-Search is more effective than the original one.
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