Use behavioral cues to suggest items and content to your users anywhere on their journey
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.
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.
Drive cross-sales and increase average cart value by showing your shoppers products that complement their current selection.
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.
Whether an item is unavailable, or a user is looking for inspiration, Algolia’s AI model compares images to your index in real time, to find related items.
Algolia AI Recommendation's Trending Items model showcases the hottest products in your catalog, capturing customer interest and boosting engagement.
Use Algolia’s AI capabilities to go beyond recommendations based on customer segment, to 1:1 marketing based on context and behavior signals.
Implement our APIs in minutes and gain easy control over rankings.
Deliver lightning-fast recommendations in milliseconds with the fastest enterprise AI search we know of.
Work with a partner who handles 30 billion records and nearly 1.7 trillion searches and recommendations a year with 99.999% availability.
Create personalized, flexible ecommerce Search & Discovery experiences your shoppers will love.
Index your catalog, put it in motion for your buyers. Increase conversion.
Build performant search experiences at scale while reducing engineering time.
Index your content, put it in motion for your users.
Increase user retention with fast and relevant search, powered by Algolia’s Search API.
Connects your customers to the right content, and your employees to the right information.
Create mobile search and discovery sessions that your users love.
Build image search for your website or app.
Rich product- and content-based customer experiences in a headless ecommerce framework.
Contextual understanding capabilities, NLP, natural-language understanding, and AI tools.
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:
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!