Algolia’s AI-powered product recommendation engine

Ecommerce recommendation system that enables rapid, scalable product discovery

With Algolia Recommend, developers can rely on our robust APIs to build the recommendations experiences best suited to meet their companies’ needs. Build recommendations carousels quickly that automatically show products or digital content to users, subscribers, and shoppers, while leveraging the power of AI. Recommend maximizes conversions, provides delightful end user engagement, and ensures repeat customer visits.

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  • Fast to Implement

    Advanced front-end libraries, API clients, and extensive documentation to help developers build, deploy, and maintain with ease and speed.

  • Fast to Adapt

    Filter, merchandise, rank, and contextualize recommendations to fit your brand and unique business goals.

  • Fast to Results

    Using Algolia Search + Recommend, leverage one platform to power discovery and drive results across your entire experience.

What can you use Algolia Recommend for?

Related Product
Frequently Bought Together
Related Content
Trends
Rules

Related Product

Help your users refine their needs.

Frequently Bought Together

Maximize the average order value.

Related Content

Increase time spent on site.

Trends

Engage your visitors from the first second.

Rules

Contextualize and merchandize your recommendations.

Related Product

Related Product

Help your users refine their needs.

Frequently Bought Together

Frequently Bought Together

Maximize the average order value.

Related Content

Related Content

Increase time spent on site.

Trends

Trends

Engage your visitors from the first second.

Rules

Rules

Contextualize and merchandize your recommendations.

Benefit from Algolia’s global, scalable infrastructure and leading developer experience

Load time less 
than 100ms

Hybrid engine solving cold start problem

6 lines of code to create a new carousel

Gymshark

+150%

Increase in order rate

Flaconi

+10%

Increase in Average Order Value

Orange

+8%

Online revenue

Flexible APIs that adapt to every use case

Auto Mercado

Auto Mercado

With the help of Algolia, Auto Mercado has been able to significantly improve the customer experience and engagement, generate new revenue streams and increase ROI.

Noski Noski

Noski Noski

Recommend is a crucial site capability when customers are unaware of many of the products available before visiting.

Gymshark

Gymshark

Leverages Algolia Recommend across 14 countries to complement the manual merchandizing done by trading teams with AI-powered recommendations.

Experience Algolia Recommend

How to surface and customize 
ecommerce recommendations in 6 lines of code

Power & personalize ecommerce product recommendations

Frequently bought together product recommendations

Frequently Bought Together

Leverage user behavior and collaborative filtering to drive cross-selling, upselling, and increase average order value

Display similar products and other relevant content

Related Products

Maximize conversions and catalog exposure by displaying similar products and other relevant content

Related content icon

Related Content

Increase time spent on time and user engagement with “Because you’ve watched” or “More on this” recommendations

Relevant product recommendations for entire catalog

Trending Products

Show up what are currently the most popular products and engage your visitors from the first second with truly dynamic home page

Trending facet icon

Trending facets

Surface your most popular categories, topics or brands and help your visitors navigate quickly toward what shouldn’t be missed out

Blending machine- and human- learning

Use customer data to optimize user experienceUse customer data to optimize user experience

Analytics

Understand your users, uncover hidden opportunities, and optimize your overall customer experience

Test your algorithm to ensure accurate product recommendation

Recommendations Simulator

Ensure your algorithm is providing the most accurate recommendations before going live

Filter results to surface the best product recommendations

Filters

A filtering method that allows you to surface the perfect recommendations for your business

Control and display product recommendations that reflect business KPIsControl and display product recommendations that reflect business KPIs

Rules

Give your business users the autonomy to apply their strategies on top of recommendations

Easy to deploy, simple to use

Algolia Recommend FAQs

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

  • Under the hood recommendations rely on supervised machine learning models and the Algolia foundation.

    For both models, the data corresponding to the past 30 days is collected. This results in a matrix where columns are userTokens and rows are objectIDs. Each cell represents the number of interactions (click and/or conversions) between a userToken and an objectID. Then, Algolia Recommend applies a collaborative filtering algorithm: for each item, it finds other items that share similar buying patterns across customers. Items would be considered similar if the same users set interacted with them. Items would be considered bought together if the same set of users bought them.

  • 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!

  • Content-based recommendations are based solely on items’ descriptions. Personalized recommendations are also based on user’s interactions and each user will see a different set of recommendations, depending on their individual preferences. Learn more here!