Related Product
Help your users refine their needs.
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.
Start building for freeGet a Recommend DemoAdvanced front-end libraries, API clients, and extensive documentation to help developers build, deploy, and maintain with ease and speed.
Filter, merchandise, rank, and contextualize recommendations to fit your brand and unique business goals.
Using Algolia Search + Recommend, leverage one platform to power discovery and drive results across your entire experience.
Help your users refine their needs.
Maximize the average order value.
Increase time spent on site.
Engage your visitors from the first second.
Contextualize and merchandize your recommendations.
Help your users refine their needs.
Maximize the average order value.
Increase time spent on site.
Engage your visitors from the first second.
Contextualize and merchandize your recommendations.
Increase in order rate
Increase in Average Order Value
Online revenue
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.
Recommend is a crucial site capability when customers are unaware of many of the products available before visiting.
Leverages Algolia Recommend across 14 countries to complement the manual merchandizing done by trading teams with AI-powered recommendations.
Experience Algolia Recommend
Leverage user behavior and collaborative filtering to drive cross-selling, upselling, and increase average order value
Maximize conversions and catalog exposure by displaying similar products and other relevant content
Increase time spent on time and user engagement with “Because you’ve watched” or “More on this” recommendations
Show up what are currently the most popular products and engage your visitors from the first second with truly dynamic home page
Surface your most popular categories, topics or brands and help your visitors navigate quickly toward what shouldn’t be missed out
Understand your users, uncover hidden opportunities, and optimize your overall customer experience
Ensure your algorithm is providing the most accurate recommendations before going live
A filtering method that allows you to surface the perfect recommendations for your business
Give your business users the autonomy to apply their strategies on top of recommendations
Curate, automate and personalize in a no code environment
Index content from any source
Start in minutes, leverage Algolia’s full capabilities
Focus on building, Algolia ensures performance and reliability at scale
Keep your users and your customer data safe
Create a new carousel using as little as 6 lines of code
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:
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!