As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results a...As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.展开更多
The concept of Webpage visibility is usually linked to search engine optimization (SEO), and it is based on global in-link metric [1]. SEO is the process of designing Webpages to optimize its potential to rank high on...The concept of Webpage visibility is usually linked to search engine optimization (SEO), and it is based on global in-link metric [1]. SEO is the process of designing Webpages to optimize its potential to rank high on search engines, preferably on the first page of the results page. The purpose of this research study is to analyze the influence of local geographical area, in terms of cultural values, and the effect of local society keywords in increasing Website visibility. Websites were analyzed by accessing the source code of their homepages through Google Chrome browser. Statistical analysis methods were selected to assess and analyze the results of the SEO and search engine visibility (SEV). The results obtained suggest that the development of Web indicators to be included should consider a local idea of visibility, and consider a certain geographical context. The geographical region that the researchers are considering in this research is the Hashemite kingdom of Jordan (HKJ). The results obtained also suggest that the use of social culture keywords leads to increase the Website visibility in search engines as well as localizes the search area such as google.jo, which localizes the search for HKJ.展开更多
目前主流开源爬虫框架在分析页面与主题领域关联性上,常采用基于关键词的量化和向量空间模型算法相融合,但融合疏忽了界面语义与特定主题间的关联,导致爬取内容与主题产生偏差。为了给金融等领域的舆情分析提供准确的数据支撑,提出一种...目前主流开源爬虫框架在分析页面与主题领域关联性上,常采用基于关键词的量化和向量空间模型算法相融合,但融合疏忽了界面语义与特定主题间的关联,导致爬取内容与主题产生偏差。为了给金融等领域的舆情分析提供准确的数据支撑,提出一种面向领域扩展主题库的爬虫及系统,通过扩展主题特征库,融合向量空间模型(Vector Space Model,VSM)与超链接主题搜索算法(Hyperlink-Induced Topic Search,HITS),优化了主题页面相关度计算,并针对股票舆情信息爬取进行仿真。结果表明,上述扩展主题型爬虫在爬取准确率和效率等方面有较好地提升,能够有效地完成领域主题信息的爬取任务。展开更多
文摘As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.
文摘The concept of Webpage visibility is usually linked to search engine optimization (SEO), and it is based on global in-link metric [1]. SEO is the process of designing Webpages to optimize its potential to rank high on search engines, preferably on the first page of the results page. The purpose of this research study is to analyze the influence of local geographical area, in terms of cultural values, and the effect of local society keywords in increasing Website visibility. Websites were analyzed by accessing the source code of their homepages through Google Chrome browser. Statistical analysis methods were selected to assess and analyze the results of the SEO and search engine visibility (SEV). The results obtained suggest that the development of Web indicators to be included should consider a local idea of visibility, and consider a certain geographical context. The geographical region that the researchers are considering in this research is the Hashemite kingdom of Jordan (HKJ). The results obtained also suggest that the use of social culture keywords leads to increase the Website visibility in search engines as well as localizes the search area such as google.jo, which localizes the search for HKJ.
文摘目前主流开源爬虫框架在分析页面与主题领域关联性上,常采用基于关键词的量化和向量空间模型算法相融合,但融合疏忽了界面语义与特定主题间的关联,导致爬取内容与主题产生偏差。为了给金融等领域的舆情分析提供准确的数据支撑,提出一种面向领域扩展主题库的爬虫及系统,通过扩展主题特征库,融合向量空间模型(Vector Space Model,VSM)与超链接主题搜索算法(Hyperlink-Induced Topic Search,HITS),优化了主题页面相关度计算,并针对股票舆情信息爬取进行仿真。结果表明,上述扩展主题型爬虫在爬取准确率和效率等方面有较好地提升,能够有效地完成领域主题信息的爬取任务。
