Vector Space Model The vector space model provides a way of comparing a multiterm query against a document. The output is a single score that represents how well the document matches the query. In order to do this, the model represents both the docum
Theory Behind Relevance Scoring Lucene (and thus Elasticsearch) uses the Boolean model to find matching documents, and a formula called the practical scoring function to calculate relevance. This formula borrows concepts from term frequency/inverse d
Field-length norm How long is the field? The shorter the field, the higher the weight. If a term appears in a short field, such as a title field, it is more likely that the content of that field is about the term than if the same term appears in a mu
一.前言 在实际使用中搜索结果中的关键词前端通常会以特殊形式展示,比如标记为红色使人一目了然.我们可以通过 ES 提供的高亮功能实现此效果. 二.代码实现 前文查询是通过一个继承 ElasticsearchRepository 的接口实现的,但是如果要实现高亮,这种方式就满足不了了,这里我们需要通过 ElasticsearchTemplate 来完成. 2.1 注入 ElasticsearchTemplate ① ElasticsearchTemplate 类简介 public class El
一.前言 上篇介绍了 ES 的同义词搜索,使我们的搜索更强大了,然而这还远远不够,在实际使用中还可能希望搜索「fanqie」能将包含「番茄」的结果也罗列出来,这就涉及到拼音搜索了,本篇将介绍如何具体实现. 二.安装 ES 拼音插件 2.1 拼音插件简介 GitHub 地址:https://github.com/medcl/elasticsearch-analysis-pinyin 2.2 安装步骤 ① 进入 ES 的 bin 目录 $ cd /usr/local/elasticsearch/bi
When we run a simple term query with explain set to true (see Understanding the Score), you will see that the only factors involved in calculating the score are the ones explained in the preceding sections: PUT /my_index/doc/1 { "text" : "q
