Guides / Personalization / Classic personalization

What is Personalization?

AI Personalization is now available in public beta. Migrate now to personalize search results based on historical and predictive user affinities.

Personalization strengthens interactive search, adding a personal layer to your relevance strategy. Adding personalized preferences to the search experience makes results more engaging for individual users.

Personalization is available on the Build and Premium pricing plans.

Advantages of Personalization

Algolia’s out-of-the-box relevance strategy treats every user the same way, but Personalization considers a user’s specific tastes. Textual relevance and business metrics are the first steps to great relevance, but Personalization lets you engage your users on their level.

Queries mean different things to different people. For example, a user who searches for “harry” and prefers children’s literature likely wants to see “Harry Potter” results appear on the first page. Users who follow politics may be more interested in seeing “Harry Truman” in their results. Personalization incorporates a user’s past behavior when determining the most relevant results.

Better relevance minimizes the user’s effort to find what they want. It also encourages users to stay on your site or app longer by exposing them to more options they’re likely to find appealing. Thanks to Google and Amazon, most users expect a digital experience where their preferences are used to tailor their results. Algolia’s Personalization feature brings this capability to your business.

How does Personalization fit into Algolia’s relevance strategy?

Effective relevance has two main goals:

  • Enabling your users to find results that match their expectations.
  • Providing results that align with your business needs.

You can address these twin goals by fine-tuning the first three layers of Algolia’s relevance strategy:

  1. Textual relevance - intelligent and robust textual matching that includes typo tolerance, synonyms, natural language processing, and various other capabilities and settings.
  2. Business relevance - ranking results according to business metrics through custom ranking.
  3. Merchandising - boosting and burying specific results or categories using Rules.

You can also enable Dynamic Re-ranking to boost trending results and categories based on the collective search data of all your users.

All these contribute, in different ways, to producing relevant results that apply equally to all your users. Personalization acts in concert with these to individualize search results. It doesn’t replace Algolia’s ranking algorithm—it refines it by changing the order of pre-sorted results to promote more relevant results to the individual user. This, in turn, can increase user engagement and drive more conversions.

Personalization kicks in after the engine computes your results’ textual relevance and applies your business relevance. If you use Rules, the engine applies them after the Personalization.

If you’ve enabled both Personalization and Dynamic Re-ranking, the engine doesn’t apply both Personalization and Dynamic Re-ranking concurrently. Instead, it applies:

  • Personalization for users with enough data to personalize the search
  • Re-ranking for users without enough data to personalize the search (such as first-time users).

The order of relevance strategies

In summary, the engine applies the relevance strategies in this order:

  1. Textual relevance (through the textual ranking criteria)
  2. Business relevance (through custom ranking)
  3. User-based preferences (through either Personalization or AI Re-Ranking)
  4. Merchandising (through Rules)

The only exception is when you’ve set your Personalization impact to 100. In that case, the engine prioritizes Personalization over business relevance. Then the engine applies the relevance strategies in this order:

  1. Textual relevance (through the textual ranking criteria)
  2. User-based preferences (through Personalization)
  3. Business relevance (through custom ranking)
  4. User-based preferences (through Dynamic Re-ranking, only if there isn’t enough data to personalize results for a particular user)
  5. Merchandising (through Rules)

Before you enable Personalization, you should simulate your Personalization strategy and A/B test the effects of Personalization first.

Further reading

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