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Personalized search

As explained in Elasticsearch in Action, Second Edition

Personalized search adapts results to an individual user's preferences, behavior, history, or context.

Personalized search adapts results to each user by learning from preferences, behavior, search history, and context. Generative AI can continually refine this understanding to produce increasingly customized results. section 1.6

Personalized search can use:

  • User preferences and past behavior to tailor result selection. section 1.6
  • Search history and current context to refine the interpretation of future queries. section 1.6

How Elasticsearch in Action, Second Edition covers it

How deep it goes
Describes the capability and its data inputs but stops short of implementation details, ranking methods, or operational procedures.
Examples
Uses preferences, behavior, search history, and current context as personalization signals.
What you can do after
Evaluate how user data and generative AI can make search results increasingly customized and adaptive.
How it connects
Connects personalized search with generative AI, predictive search, advanced search, and full-text search.
Cover of Elasticsearch in Action, Second Edition
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This explainer was generated by Manning's knowledge graph from the text of Elasticsearch in Action, Second Edition. The section links point to the parts of the book it draws on.