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Meilisearch offers two ways to tailor results to individual users:
  • Personalized search re-ranks search results at query time based on a user profile you provide, so each user sees the most relevant results for them.
  • Recommendations use the /similar endpoint to find documents semantically close to a given item, powering features like “More like this” or “Related items”.
Both features work alongside full-text search and hybrid search. Not everyone searches the same way. Personalized search lets you adapt relevance to each user’s preferences, behavior, or intent. For example, in an e-commerce site, someone who often shops for sportswear might see sneakers and activewear ranked higher when searching for “shoes”. A user interested in luxury fashion might see designer heels or leather boots first instead.

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How it works

  1. Generate a user profile: "The user prefers genres like Documentary, Music, Drama"
  2. Submit the profile together with the search request
  3. Meilisearch retrieves documents based on the query as usual
  4. The re-ranking model reorders results based on the user profile

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Recommendations

Once you have configured an embedder, you can use the /similar endpoint to find documents that are semantically close to a reference document. This requires no additional configuration beyond the embedder itself. Typical use cases include “Customers also viewed” on product pages, “Related articles” on content platforms, and “Because you watched X” on media streaming services.

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Use cases

  • E-commerce: Surface products aligned with a shopper’s purchase history, brand preferences, or browsing behavior. Recommend similar products on detail pages.
  • Content platforms: Rank articles, videos, or podcasts based on the topics a user engages with most. Show related content alongside the current item. Combine with analytics to measure impact.
  • Marketplace search: Tailor listings to a buyer’s location, budget range, or past interactions so the most relevant offers appear first.

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Next steps

Personalized search

Configure and perform your first personalized search

Recommendations

Build a recommendation system with the similar documents endpoint

Generate user context

Build user profiles from behavior data

Personalize e-commerce search

Step-by-step guide for personalizing product search results