Build a unique AI-powered product recommendation engine for personalized ecommerce experiences
With Algolia Recommend, developers can use a simple and flexible API to build machine learning powered recommendations on your company’s digital storefronts using as few as 6 lines of code, driving better conversion rates, increasing average order value, and boosting customer satisfaction & retention. Algolia Recommend comes with advanced flexibility so you can display completely customized product recommendations on your online stores.Start building for freeGet a search audit
- Build unique experiences
Filter, merchandise, rank and contextualize recommendations to fit your brand and unique business goals.
- Implement & iterate in no time
Advanced front-end libraries, API clients, and extensive documentation to help developers build, deploy, and maintain with ease.
- Rely on a unified platform
Using Algolia Search + Recommend, leverage the same product catalog, merchandising logic, and analytics across search, navigation and real-time recommendations.
An Ecommerce recommendation system that enables rapid, scalable product discovery
Power & personalize ecommerce product recommendations
Frequently Bought Together
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
Relevant product recommendations for the entire catalog, even the long tail
Full control, insights for everyone on your team
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
Get control over the recommended products to reflect your business KPIs
Easy to integrate, up and running in minutes
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.
Our recommendation engine is language-agnostic: it supports alphabet-based and symbol-based languages (such as Chinese, Japanese or Korean).
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 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!