An AI recommendation system uses shopper behavior and product data to suggest items a customer is likely to want. An online store could recommend similar jackets, socks to go with running shoes, or a coffee blend based on a previous purchase.
Product suggestions based on browsing history and past purchases give shoppers personalized shopping experiences. They also give you opportunities to increase order value and encourage repeat purchases.
This article will explore how AI recommendation systems work, including how the main types differ, and where to place recommendations in your store to help customers find their next purchase.
What is an AI recommendation system?
An AI recommendation system, also known as a recommendation engine, uses machine learning algorithms to suggest relevant products, services, or content to people online.
Recommendation engines provide AI personalization by analyzing a user’s reviews, ratings, searches, and past purchases alongside product attributes. Businesses use them across entertainment, finance, marketing, and ecommerce.
In a 2025 Shopify survey, 75% of store owners said they use AI tools.* The survey polled 500 merchants with at least two years of experience selling on Shopify. Product recommendations are one application of AI in ecommerce.
For example, an ecommerce store might display suggestions based on products that customers frequently purchase together. On its storefront, Gymshark has recommendation collections and product suggestions.
How AI recommendation systems work
AI recommendation systems generate and update suggestions through three stages:
- Collecting signals. The system gathers customer data, such as product views, cart additions, and purchases. It can also draw on product details, such as category, price, and material, to understand the items in your catalog.
- Identifying patterns. Machine learning algorithms identify similarities between products or patterns in shopping behavior to predict what someone will want. For example, if customers frequently buy a grinder with a particular coffee maker, the system could recommend that grinder to another shopper viewing the coffee maker.
- Continuously learning from shopper behavior. New clicks and purchases provide feedback for future recommendations. The system uses this activity to update its predictions as shoppers’ interests change. Depending on the tool, suggestions can adjust during a visit or after scheduled model updates.
How an AI recommendation system can increase your sales
AI recommendation systems can increase sales by helping shoppers discover relevant products faster, increasing average order value (AOV) with cross-sells and bundles, and encouraging repeat purchases.
In a February 2026 survey of online shoppers in Australia, the UK, and the US, 53% said they were more likely to keep shopping with a brand that made relevant product suggestions. The survey polled 1,050 people who subscribed to email or text marketing.
Beauty company Orveon Global migrated to Shopify from the Salesforce Commerce Cloud, and reported larger orders after integrating the Nosto AI Search & Discovery recommendation system. “Immediately—and this was consistent across every brand—we saw an AOV lift between 10% to 15% for each brand. So I think our ability to cross-sell with Nosto live drove an immediate sales lift,” says Carney Nir, vice president of ecommerce and site experience at Orveon Global.
Why recommendations drive revenue
Product suggestions can influence what shoppers discover, how much they buy, and whether they return:
- Faster product discovery. Relevant suggestions help shoppers find items they would otherwise miss.
- Higher average order value. Cross-selling encourages shoppers to add complementary products to an order.
- More repeat purchases. Recommendations can also encourage upselling, such as suggesting a higher-capacity coffee maker to someone viewing an entry-level model.
Product and cart recommendations appear on your storefront before checkout. Suggestions within checkout require separate customization.
On Shopify, apps that modify the information, shipping, or payment steps require the Shopify Plus plan. Some apps for post-purchase, thank you, and order-status pages are available on Basic or higher. Check the app’s checkout compatibility before planning these placements.
Where to use an AI recommendation system in your store
Plan your ecommerce merchandising around the shopper’s activity, then monitor how each recommendation placement affects purchases:
| Placement | What to recommend | Metrics to track |
|---|---|---|
| Homepage and collection pages | Products based on browsing history, with bestsellers as a fallback for new visitors | Recommendation click rate, conversion rate, revenue per visitor |
| Product pages | Similar alternatives or products that complement the item being viewed | Recommendation click rate, add-to-cart rate, purchase rate |
| Cart pages | Accessories, refills, or frequently bought together bundles | Add-on purchase rate, average order value, checkout completion rate |
| Post-purchase pages | Products that complement the completed order | Offer acceptance rate, additional revenue per order |
| Email and follow-up messages | Suggestions based on previous purchases or replenishment timing | Click-through rate, revenue per delivered email, repeat purchase rate |
For example, Finisterre uses Shopify customer insights with its customer data platform, Ometria, to tailor its marketing to purchase history and location. It also uses content personalization to customize welcome messages according to a customer’s country and the channel they came through.
Common recommendation mistakes
Review the products your system suggests and correct these problems:
- Irrelevant matche.. Check product descriptions, categories, and tags. Correct inaccurate data and manually replace poor suggestions where your tool allows it.
- Unavailable products. Keep inventory data current and exclude products shoppers cannot order. Make sure that the recommended sizes or variants are available.
- Repeated purchase reminders. Update customer records after an order. Exclude recently purchased products from acquisition messages and time replenishment suggestions to the expected buying cycle.
- Too many competing suggestions. Test a smaller selection in one placement. Review whether the recommendations distract shoppers from adding an item or completing checkout.
- Weak recommendations for new visitors. Provide a fallback, such as popular products or items related to the page being viewed, until the system has enough behavioral data.
Shopify’s Search & Discovery app lets you customize related and complementary products on compatible product pages. Its eligibility rules also filter recommendations based on factors such as product availability and whether an item is already in the shopper’s cart.
Measure whether recommendations increase sales
Shopify Search & Discovery reporting shows recommendation click rate, purchase rate, and products with low recommendation engagement. The app displays the past 30 days. Open the full reports through Analytics > Reports to review other date ranges.
These reports identify suggestions that attract clicks and purchases. Shopify’s product recommendation conversion report tracks its product-page recommendations and excludes products added or purchased through a Buy Button or third-party app. Check your recommendation app’s reporting for other placements.
To estimate how many sales recommendations add, run an A/B test:
- Randomly assign shoppers to two groups. Show the new recommendations to one group and keep the existing experience for the control group.
- Keep the comparison consistent. Keep shoppers in their assigned groups and run both versions over the same dates with consistent prices and promotions.
- Evaluate the full groups. Set the test duration and required sample size before launch. Compare conversion rate and revenue per visitor across everyone assigned to each group, including shoppers who never click a recommendation. Review average order value and profit margins alongside those results.
A randomized field experiment on a mobile food-delivery platform found that users who received AI-generated search suggestions for 30 days placed more orders and browsed more sellers than a control group with autocomplete alone. Sales volume increased by 1% to 2%. Store search can also use semantic search to match results to the meaning of a shopper’s query.
Types of AI recommendation systems
AI recommendation systems generate suggestions from product attributes, patterns in shopper behavior, or a combination of both. The data available in your store helps determine which approach to test.
By recommendation approaches using contextual and knowledge-based systems are forecast to expand at 20% CAGR to 2030, according to Mordor Intelligence.
| Type | How it works | Data needed | Main limitation | Advantage |
|---|---|---|---|---|
| Content-based filtering | Suggests products with attributes similar to items a shopper likes | Detailed product information and some indication of the shopper’s interests | Can repeatedly suggest similar products | |
| Collaborative filtering | Finds patterns across shoppers’ interactions with products | Browsing, purchase, or rating history across customers | Struggles with new shoppers or products that have little interaction data | |
| Hybrid filtering | Combines product attributes with patterns in shopper behavior | Product information and customer interaction data | Adds complexity when combining and maintaining data sources | |
| LLM-based | Uses a shopper’s history and matches that against product descriptions, attributes, and reviews | Purchase and browse history, stated preferences, cart activity, product details, plus clicks of previous recommendations | May recommend nonexistent, out-of-stock, or other products that don’t match shopper’s needs |
Content-based filtering
A recommendation engine using content-based filtering suggests products with characteristics similar to items a shopper has viewed, purchased, or rated positively. Those characteristics can include category, material, price range, and product tags.
This approach can work for stores with a small customer base because it does not depend on other shoppers’ behavior. For example, a specialty tea store could recommend loose-leaf blends based on the flavor profile, caffeine level, and origin of a tea the customer already likes.
Detailed product information helps the system identify useful matches. Its main limitation is that suggestions tend to reflect interests the shopper has already expressed.
Collaborative filtering
Collaborative filtering predicts preferences from patterns in how customers browse, buy, or rate products. It can identify shoppers with similar tastes or products that attract similar groups of customers.
For example, a clothing brand could recommend a jacket to someone who bought jeans because other customers frequently purchased both. The jacket doesn’t need to share the jeans’ product attributes for the system to identify a relationship.
This approach needs enough customer activity to find useful patterns. New shoppers and products with little interaction history can be harder to recommend accurately, a limitation known as the cold-start problem.
Hybrid filtering
Hybrid recommendation systems combine content-based and collaborative filtering. They consider both product characteristics and patterns in customer behavior when generating suggestions.
For example, a cookware store could recommend a stainless steel pan based on a customer’s material preferences and purchases made by shoppers with similar tastes. Product details can also help the system assess new items that have little purchase history.
Some hybrid systems use deep learning to find patterns across product details and shopping behavior. These AI models can create vector embeddings, or lists of numbers that represent product characteristics or shopper preferences. The system compares these representations to predict which products a customer is likely to want.
Combining these methods gives the system more information to work with, but adds complexity to data preparation and maintenance. Test the results against your existing recommendations to determine whether the added complexity produces better sales performance.
LLM-based filtering
An LLM-based recommender reads what a shopper has bought, browsed, and typed into search (or website chat logs), and matches that to product descriptions and reviews to suggest items.
For example, if a shopper types “smart gift under $75 for sister who loves baking” into search, the LLM-based recommendation system may surface recipe books, measuring spoons, or certain rolling pins, and also explain why a baker would like or need the recommended product.
LLM-based filtering works well with natural language searches, but can also hallucinate products that don’t exist. It can also be costlier and run slower than other recommendation systems.
How to choose an AI recommendation system for your store
Before comparing tools, decide where recommendations will appear, what customer data you can provide, and who will manage the system.
In the Census Bureau’s reporting period ending May 3, 2026, about 14% of US retail businesses reported using AI, below the 19.8% rate across all industries. Deloitte’s 2026 outlook found that 67% of 330 retail executives surveyed globally expected AI-driven personalization capabilities within the following year.
Compare these three approaches against your store’s requirements:
| Approach | Store fit | Features and tradeoffs |
|---|---|---|
| Shopify’s built-in recommendations | Stores needing product-page suggestions with limited setup and development work | The free Search & Discovery app lets you customize automatically generated related products and manually select complementary items. Displaying them requires a compatible theme. |
| Recommendation apps | Stores adding recommendations across more pages or channels and running regular experiments | Apps can add personalized placements, merchandising controls, and A/B testing. Features and fees vary, so confirm available placements, email integrations, and traffic- or sales-based charges. |
| Custom or managed systems | Stores with specialized requirements, complex data integrations, and an engineering team or implementation partner | Build your own system or integrate a service such as Amazon Personalize. Managed services operate the infrastructure and your team integrates data, tests recommendations, and monitors results. Budget for development and usage. |
In a 2025 Shopify survey of 500 established merchants, 29% of store owners who weren’t using AI said they weren’t sure what AI tools could do.* Ask vendors to demonstrate recommendations using your own catalog. You can then see which suggestions are automatic, which require manual rules, and whether your existing data meets the tool’s requirements.
The demo is also a chance to check how the system treats new visitors and unavailable products. A store with little purchase history, for example, needs recommendations that draw on product information while it collects more customer data.
Request a cost estimate based on your traffic, including setup, integration, and ongoing maintenance. With those costs established, a test of one placement against a control group can help you judge whether the additional profit justifies expanding.
Put your AI recommendation system into action
Test product recommendations on one of your most-visited product pages. Shopify’s Search & Discovery app lets you review automatically generated suggestions and add your own selections.
For each product, you can select up to 10 complementary products and 10 related products. Related recommendations can display your selections alone or alongside automatically generated suggestions. Add the relevant section or block to your product page in a compatible theme.
Review recommendation clicks and purchases in Search & Discovery reporting. Then compare sales against a control group to determine whether the recommendations generate additional revenue before expanding to other placements.
*Based on a November 2025 survey of 500 Shopify merchants conducted in English across Australia, Canada, the United Kingdom, Ireland, New Zealand, and the United States. Respondents were established merchants with two or more years on the platform. Results reflect the experiences of this specific sample and may not be representative of all merchants.
AI recommendation FAQ
What is the most popular recommendation algorithm?
Collaborative filtering is a widely used recommendation approach. It suggests products based on patterns in customers’ purchases, ratings, or browsing behavior. Many systems combine it with content-based filtering, which uses product attributes, to produce hybrid recommendations.
How much data do you need for product recommendations?
Content-based recommendations can work with detailed product information and a shopper’s current activity. Collaborative filtering needs interaction history across customers to identify patterns. Check your provider’s data requirements and test whether the suggestions are relevant before expanding your setup.
How can you create an AI recommendation system?
Install a recommendation app, integrate a managed service, or build a custom model. For a custom system, collect and clean product and customer interaction data, train a recommendation model, and integrate its suggestions into your storefront. Test it against a control group and update it as shopping behavior changes.
What are common drawbacks of recommendation systems?
Recommendation systems can produce irrelevant suggestions when product data is inaccurate or customer history is limited. They can also repeatedly recommend similar items, reducing variety. Other drawbacks include implementation costs, ongoing maintenance, and privacy risks when collecting and storing customer data.
What is a good first step for a small store?
Add related product recommendations to a frequently visited product page using a compatible Shopify theme. Review the suggestions in the free Search & Discovery app and add custom selections where needed. Track recommendation clicks and purchases before testing additional placements.
