AI Recommendations

Suggestions that delight

Use behavioral cues to suggest items and content to your users anywhere on their journey

Get a demo
Get started for free
PRODUCT_Algolia-Recommend

A richer user journey

Inspire undecided visitors

No matter where your users are in their journey, AI Recommendations drives higher site engagement and cart/conversion metrics. 

Algolia’s AI surfaces personal recommendations in a smart carousel, to match user affinities. Advanced filters let you apply more granular preferences at a user or customer segment level.

Drive cross sell with AI Recommendations

Maximize catalog exposure and drive cross-sell

A seamless integration with your backend CMS and product catalogs will help expose the full breadth of your products or content to users throughout their journey.

Precise recommendation models and tools

Frequently Bought Together

Drive cross-sales and increase average cart value by showing your shoppers products that complement their current selection.

Read the docs

Algolia's 'Looking Similar' feature uses image recognition technology to identify related items, helping users discover products that fit a specific theme, vibe or mood and increase cart size through additional purchases.

Read the docs

Use Algolia’s AI capabilities to go beyond recommendations based on customer segment, to 1:1 marketing based on context and behavior signals.

Learn more

More than 17,000 customers in 150+ countries trust Algolia

Decathlon
Gymshark
Orange
Noski Noski
Flaconi

The one-stop shop for your AI recommendations

Easy to use

Implement our APIs in minutes and gain easy control over rankings.

Fast

Deliver lightning-fast recommendations in milliseconds with the fastest enterprise AI search we know of.

Scalable

Work with a partner who handles 30 billion records and nearly 1.7 trillion searches and recommendations a year with 99.999% availability.

Addressing a wide range of industries

Industry_B2C-commerce

Create personalized, flexible ecommerce Search & Discovery experiences your shoppers will love.

Read more on B2C ecommerce

Index your catalog, put it in motion for your buyers. Increase conversion.

Read more on B2B ecommerce

Build performant search experiences at scale while reducing engineering time.

Read more on marketplaces

Index your content, put it in motion for your users.

Read more on media

Increase user retention with fast and relevant search, powered by Algolia’s Search API.

Read more on SaaS

Solutions for multiple use-cases

Solutions_Enterprise

Rich product- and content-based customer experiences in a headless ecommerce framework.

Read more on headless ecommerce

AI Recommendations FAQs

  • Really fast. Most recommendation requests will take from 1 to 20 milliseconds to process.

  • Under the hood recommendations rely on multiple algorithms and models, depending on the use case.

    The Frequently Bought Together/Complementary recommendations  is trained on past 30d user interactions and it uses a collaborative filtering algorithm for the Relaxed variant  and a statistical approach for the Strict variant.

    The Related Products/Alternative recommendations model uses a collaborative  filtering algorithm, a content-based model or a hybrid one, based on the available data for training (user interactions, product attributes or both).

    Both Trending items and Trending facets value models compute the recommendations based on the increase in the number of purchases in the last days per product, respectively per facet value.

    The Looking similar model uses the products  images to recommend the most visually similar products in the catalog.

  • Getting recommendations is a four-step process:

    1. Capture your users’ conversion events
    2. Send your data to Algolia
    3. Train the models with the push of a button
    4. Add recommendations to your UI
  • Our recommendation engine is language-agnostic: it supports alphabet-based and symbol-based languages (such as Chinese, Japanese or Korean).

  • Essentially a recommendation engine will analyse interactions of users with different items to draw links between those items. Deep dive here.

  • An example of a recommendation engine is a product recommendation engine for ecommerce. It will analyse what products shoppers buy together or what products shoppers interact with in a short amount of time, to generate “Frequently Bought Together” or “Related Products” recommendations. Learn more here!

  • The key components of a high-performance recommender system are: Data Sources, Feature Store, Machine Learning Models, Predictions & Actions, Results & Metrics. More details in this dedicated series.

  • The best way to improve a recommendation engine is to make sure you’re feeding it qualitative data: user interactions and items. Additionally there are filters that you can apply to the recommendations that are being generated. Ultimately, key performance indicators must be accurately tracked in order to identify areas of improvement.

  • The most obvious operational goal of using a personalized recommender system is to recommend items that are relevant to the user, as people are more likely to buy items they find attractive. Learn more about personalized recommendations and their benefits here!