The webpage re-ranking is a challenging task while retrieving the webpages based on the query of the user. Even though the webpages in the search engines are ordered depends on the importance of the content, retrieving the necessary documents based on the input query is quite difficult. Hence, it is required to re-rank the webpages available in the websites based on the features of the pages in the search engines, like Google and Bing. Thus, an effective Rider-Rank algorithm is proposed to re-rank the webpages based on the Rider Optimization Algorithm (ROA). The input queries are forwarded to different search engines, and the webpages generated from the search engines with respect to the input query are gathered. Initially, the keywords are generated for the webpages. Then, the top keyword is selected, and the features are extracted from the top keyword using factor-based, text-based and rank-based features of the webpage. Finally, the webpages are re-ranked using the Rider-Rank algorithm. The performance of the proposed approach is analyzed based on the metrics, such as F-measure, recall and precision. From the analysis, it can be shown that the proposed algorithm obtains the F-measure, recall and precision of 0.90, 0.98 and 0.84, respectively.

中文翻译：

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基于Rider-Rank算法的特征提取，用于在搜索引擎中重新排名网页

在基于用户的查询检索网页时，网页重新排名是一项艰巨的任务。即使搜索引擎中的网页的排序取决于内容的重要性，但基于输入查询来检索必要的文档还是非常困难的。因此，需要根据搜索引擎（例如Google和Bing）中页面的功能来重新排序网站中可用的网页。因此，提出了一种有效的Rider-Rank算法，以基于Rider优化算法（ROA）对网页进行重新排名。将输入查询转发到不同的搜索引擎，并收集从搜索引擎针对输入查询生成的网页。最初，为网页生成关键字。然后，选择top关键字，并使用网页的基于因子，基于文本和基于排名的特征从top关键字中提取特征。最后，使用Rider-Rank算法对网页进行重新排名。基于F-措施，召回率和精度等指标分析了所提出方法的性能。从分析中可以看出，该算法的F值，查全率和查准率分别为0.90、0.98和0.84。