基于URL类型优先级的入口页面查询算法

入口页面(主页)查询结果只有一个，并且用户的查询词常常是简短的页面名称，由于它要求更高的精准度，一般认为是较为困难的. 依据语言模型分析，挖掘出对中文入口页面(entry page)检索有意义的查询域作为基准检索的内容域，同时考虑到非内容网页优先级(URL type等)特征的重要性，建立综合内容域和非内容网页特征的检索模型. 通过URL类型优先级(URL type prior)的概率统计，发现入口页面和其相关的子页面之间存在比较大的联系. 据此提出基于相关子页面的入口页面提取算法PERS(page extracted from relevant sub page). 对比实验数据表明，PERS算法对检索的性能有较大提高.

Entry page search algorithm based on URL-type prior probabilities

Entry page(home page) retrieval has the goal to retrieve just one right document,and the queries are usually short Web-page names.As a result,finding precisely an entry page with a high initial is quite difficult.According to unigram language model,the authors extract the field of Web page contents for baseline retrieval,which are useful for finding Chinese entry page,and then we build a new model combined content-field and non-contents features of Web pages(e.g.URL-type prior,proved to have the strongest predictive power).According to the prior probabilities of URL-type,the relationship between entry page and its sub-pages is discovered.Based on the relationship,we propose a new algorithm that entry page is extracted from relevant sub-pages(PERS).At last,we get the result from re-rank,and achieve a great advance on performance of entry page retrieval by using PERS.