为了提高PageRank算法的准确性,从网络用户对已知网页进行评价的角度引入网页等级,从网页链接分析的角度解决权威性需求.结合网页链接分析和页面内容分析提出一种改进的PageRank算法PRP(PageRank based on Page-level).实验证明,算法为...为了提高PageRank算法的准确性,从网络用户对已知网页进行评价的角度引入网页等级,从网页链接分析的角度解决权威性需求.结合网页链接分析和页面内容分析提出一种改进的PageRank算法PRP(PageRank based on Page-level).实验证明,算法为扩展PageRank提供了广阔的空间,通过选择合适的参数page-level,可以提高传统PageRank算法的网页排序的准确性,有效防止恶意链接对pagerank排序值(PR值)造成的影响.展开更多
The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in thi...The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.展开更多
文摘为了提高PageRank算法的准确性,从网络用户对已知网页进行评价的角度引入网页等级,从网页链接分析的角度解决权威性需求.结合网页链接分析和页面内容分析提出一种改进的PageRank算法PRP(PageRank based on Page-level).实验证明,算法为扩展PageRank提供了广阔的空间,通过选择合适的参数page-level,可以提高传统PageRank算法的网页排序的准确性,有效防止恶意链接对pagerank排序值(PR值)造成的影响.
文摘The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.
