Pondération et calcul de pertinence

When you search for something, you don't just want any results; you want the best results. This is where relevancy comes in!

Relevancy helps search engines, like Elasticsearch, decide which documents are most important or 'relevant' to your query and present them first.

In Elasticsearch, every document that matches your search query gets a numeric value called the _score. This score represents how relevant that document is to your query.

• A higher _score means the document is considered more relevant.
• Elasticsearch uses algorithms (like BM25) to calculate this score, considering factors like how often a term appears and its uniqueness.

While Elasticsearch calculates relevancy automatically, you often want to guide it. This is where boosting comes in!

Boosting allows you to manually increase or decrease the importance of specific query clauses or fields, directly influencing the _score of matching documents.

You can apply a boost parameter to an entire query clause. A boost value greater than 1.0 increases its impact, while a value less than 1.0 decreases it.

The default boost value is 1.0, meaning no special emphasis.

Let's say you're searching for 'coding' and want matches in the description field to be twice as important as other parts of your query.

You can add "boost": 2 to that specific match clause:

Often, a match in one field is inherently more valuable than a match in another. For example, finding a keyword in a document's title is usually more relevant than finding it in its content.

Field boosting lets you specify this importance directly within your query.

Using the ^ (caret) operator after a field name, you can assign a boost factor. Here, a match in title is 3 times more important than a match in description:

For even more control over relevancy, Elasticsearch offers the function_score query. This powerful query type allows you to apply custom scoring logic to documents.

You can factor in things like a document's popularity, recency, or specific numeric field values to influence its _score.

Here's a simple function_score example. It searches for 'elastic' and then boosts the score based on the views_count field, multiplying the base score by a factor derived from views_count.

Test your understanding of boosting and relevancy in Elasticsearch!

Great job! You've learned how to take control of relevancy in Elasticsearch.

• The _score dictates a document's importance.
• Query boosting lets you emphasize entire query clauses.
• Field boosting prioritizes matches in specific fields.
• The function_score query provides advanced, custom relevancy adjustments.

By using these techniques, you can ensure your users always find the most relevant information first!