Search result ranking is the core component of search engine, which largely determines the quality of search engine and whether users accept it or not. Although search engines integrate hundreds of ranking factors in the actual result ranking, the two most important factors are the content relevance between user queries and web pages and web page links.

关于网页链接分析算法在有详述，本章主要介绍的是:给定用户搜索词，如何从内容相关性的角度对网页进行排序。

The web page link analysis algorithm is described in detail. This chapter mainly introduces how to sort web pages from the perspective of content relevance given user search terms.

判断网页内容是否与用户查询相关，这依赖于搜索引擎所采用的检索模型。关于检索模型的研究，从信息检索学科建立之初就直是研 究，到目前为止，已经提出了多种各异的模型，本章将介绍其中更重要的几种检索模型:布尔模型、向量空间模型、概率模型、语言模型及更近几年兴起的机器学习排序算法。

Judging whether the web content is related to user query depends on the retrieval model adopted by the search engine. The research on retrieval models has been the focus of research since the establishment of information retrieval discipline. So far, many different models have been proposed. This chapter will introduce the most important retrieval models: Boolean model, vector space model, probability model, language model and machine learning ranking algorithm in recent years.

尽管检索模型多种多样，但其在搜索引擎中所处的位置和功能是相同的，给出了一个搜索引擎计算内容相似性的框架。当用户产生了信息需求后，构造查询词，以此作为信息需求的具体体现，搜索引擎在内部会对用户的查询词构造内部的查询表示方法。

Although there are many retrieval models, their position and function in search engine are the same. A framework for search engine to calculate content similarity is given. When users have information needs, query words are constructed as the specific embodiment of information needs. Search engines will internally construct internal query representation methods for users' query words.

于海量的网页或者文档集合，对每个文档，在搜索系统内部也有相应的文档表示方法。搜索引擎的核心是判断哪些文档是和用户需求相关的，并按照相关程度排序输出，所以相关度计算是将用户查询和文档内容进行匹配的过程，而检索模型就是用来计算内容相关度的理论基础及核心部件。

For a large number of web pages or document collections, there are corresponding document representation methods in the search system for each document. The core of search engine is to judge which documents are related to user needs, and sort the output according to the degree of correlation. Therefore, correlation calculation is the process of matching user query with document content, and retrieval model is the theoretical basis and core component used to calculate content correlation.

什么样的检索模型是个好模型呢?用户发出查询词Q后，我们可以把要搜索的文档集合按照“是否相关”及“是否包含查询词"两个维度，将其划分为4个象限，其中，象限的文档出现了用户查询词同时被用户判定为相关的;第二象限的文档不包含用户查询词但是被用户判断为相关的;第三象限的文档出现了用户查询词但被用户判定为不相关的;而第四象限的文档则是不包含用户查询词且被用户判断为不相关的

What kind of retrieval model is a good model? After the user sends out the query word Q, we can set the documents to be searched according to "relevant" and "whether to include query words" " The two dimensions are divided into four quadrants. The documents in the first quadrant contain user query words and are judged as relevant by the user; the documents in the second quadrant do not contain user query words but are judged as relevant by the user; the documents in the third quadrant contain user query words but are judged as irrelevant by the user; while the documents in the fourth quadrant do not contain user query words and are judged as irrelevant by the user The user determines that it is irrelevant

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