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ElasticSearch Blueprints

You're reading from   ElasticSearch Blueprints A practical project-based guide to generating compelling search solutions using the dynamic and powerful features of Elasticsearch

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781783984923
Length 192 pages
Edition 1st Edition
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Toc

Table of Contents (10) Chapters Close

Preface 1. Google-like Web Search 2. Building Your Own E-Commerce Solution FREE CHAPTER 3. Relevancy and Scoring 4. Managing Relational Content 5. Analytics Using Elasticsearch 6. Improving the Search Experience 7. Spicing Up a Search Using Geo 8. Handling Time-based Data Index

How scoring works

Scoring is the process where we determine how well a query matches against a document. This can be based on the relevance of the query against the text it matches or based on a logic that we are interested in. For example, during a restaurant search on a site like Zomato, the closer the restaurant is, the better the match. In another instance, in a news search, a mix of recent news and a specific topic would give a better match.

Note

Scoring can be based on the query or on a custom logic.

We will look at how custom logic can be introduced in scoring in the later part of this chapter. In this section, we will discuss how scoring is done on text based on the query text.

There are three main factors that determine the score of a query against a text field, which are as follows:

  1. Term frequency: Let's assume that we are searching for the word elephant, we get the following results:
    • Document-1: There are two types of elephants, African elephant and Indian elephant.
    • Document-2...
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