Key Concepts of Relevance Scoring
- Term Frequency (TF): Measures how often a term appears in a document. The more frequently a term appears, the higher its contribution to the relevance score.
- Inverse Document Frequency (IDF): Measures the importance of a term across all documents. Terms that appear in many documents have lower IDF values, reducing their impact on the relevance score.
- Field Length Normalization: Adjusts the score based on the length of the field. Longer fields may dilute the impact of term frequency.
Relevance Scoring and Search Relevance in Elasticsearch
Elasticsearch is a powerful search engine that good at full–text search among other types of queries. One of its key features is the ability to rank search results based on relevance. Relevance scoring determines how well a document matches a given search query and ensures that the most relevant results appear at the top.
In this article, we will understand relevance scoring in Elasticsearch with detailed examples and outputs to make the concepts simple and easy to learn.