When people shop online, they rarely see every product available in a store.
Instead, e-commerce websites usually show shoppers a selection of products based on searches, categories, previous activity, product information, and other signals.
Increasingly, artificial intelligence is helping determine which products appear in those recommendations.
AI-powered recommendation systems can analyze large amounts of information and identify patterns that may indicate which products are relevant to a particular shopper. These systems can influence product suggestions on retail websites, shopping apps, marketplaces, and other digital platforms.
For consumers, this can make online shopping more convenient. A shopper may discover products they would not have found through a traditional search.
At the same time, recommendations can affect what consumers notice and which products they consider. Understanding how these systems work can make it easier to interpret recommendations rather than simply accepting them at face value.
This guide explains how AI product recommendations work, what information they can use, why different shoppers may receive different suggestions, and what consumers should know about personalized shopping experiences.
What Are AI Product Recommendations?
AI product recommendations are suggestions generated by systems that use artificial intelligence or machine learning techniques to identify products that may be relevant to a shopper.
These recommendations can appear in many places across an online shopping experience.
- Homepages
- Search results
- Product pages
- Shopping carts
- Checkout pages
- Mobile shopping apps
- Email communications
A recommendation might appear as a message such as “You may also like,” “Recommended for you,” or “Customers also viewed.”
The wording varies between websites, but the basic idea is similar: the system selects a group of products that it believes may be relevant to the consumer.
AI can help automate this process by analyzing relationships among products, shoppers, and shopping activity.
For consumers, recommendations can make product discovery easier because they can surface options without requiring another manual search.
How AI Recommendation Systems Work
AI recommendation systems generally work by analyzing available information and estimating which products may be relevant to a particular shopper or situation.
A simplified recommendation process can look like this:
- The system collects available shopping and product information.
- The system identifies patterns and relationships in that information.
- The system evaluates products that could be relevant.
- The system ranks potential recommendations.
- The shopping platform displays selected recommendations.
- The system may learn from subsequent interactions.
The process can involve very large datasets.
For example, an e-commerce platform may have information about millions of products and many different shopping interactions. AI can help process these relationships much faster than a person could manually.
The recommendation does not necessarily mean that a product is objectively the best choice.
It means the system has identified the product as potentially relevant based on the information and signals available to it.
This distinction is important when evaluating personalized recommendations.
What Information Does AI Use?
There is no single set of information used by every recommendation system.
The data available to an AI system depends on the retailer, shopping platform, technology, and consumer settings.
Possible signals can include:
- Products viewed
- Products searched for
- Products purchased
- Products added to a shopping cart
- Products saved or favorited
- Product categories explored
- Product attributes
- Products frequently purchased together
- Current shopping activity
Some systems may also consider information related to a consumer’s account or preferences.
However, consumers should not assume that every platform uses every possible data source.
Recommendation systems vary considerably.
For more information about how shopping websites use recommendation technology, see How Product Recommendations Work on Shopping Websites.
How Browsing History Can Affect Recommendations
Browsing activity can provide useful signals about what a shopper may currently be interested in.
For example, if a consumer views several products within the same category, a recommendation system may identify that category as relevant.
A shopper researching office chairs could subsequently encounter recommendations for other office chairs, related accessories, or products with similar characteristics.
The system may interpret repeated activity as a stronger signal than a single accidental page visit.
Browsing behavior can therefore influence the products displayed during a shopping session.
However, browsing history does not necessarily represent a permanent preference.
A consumer may research a product for someone else, compare options without intending to buy, or simply visit a page by mistake.
This is one reason recommendations can occasionally appear irrelevant.
How Purchase History Can Influence Recommendations
Previous purchases can provide another signal for recommendation systems.
If a consumer has purchased products from a particular category, a platform may identify related products that could be relevant in the future.
For example, a previous purchase could be associated with replacement items, accessories, complementary products, or other products in the same category.
Purchase history can also help systems understand longer-term shopping patterns.
However, a previous purchase does not necessarily mean a consumer wants the same type of product again.
A shopper may purchase a gift, replace an item only once, or change their preferences over time.
AI systems attempt to account for these patterns, but recommendations can still be imperfect.
How AI Finds Similar Products
AI recommendation systems can identify products that share certain characteristics.
These characteristics can include product category, brand, specifications, materials, size, functionality, or other attributes.
Suppose a shopper views a particular backpack.
A recommendation system might identify other backpacks with similar characteristics.
It could also identify products that appeal to shoppers who viewed or purchased the same backpack.
These are two different approaches.
Product similarity
Product similarity focuses on characteristics shared by the products themselves.
Behavioral similarity
Behavioral similarity focuses on what shoppers tend to do when interacting with those products.
AI systems can combine multiple approaches to create recommendations.
This can help explain why a recommendation sometimes looks different from the original product while still being considered relevant by the system.
What Does “Frequently Bought Together” Mean?
“Frequently Bought Together” is a common type of recommendation based on relationships between products.
The underlying idea is that certain products are often purchased during the same shopping journey.
For example, consumers buying a particular type of electronic device may also purchase a compatible accessory.
A shopping platform can analyze purchase patterns and identify products that frequently appear together.
This information can then be used to suggest related items to future shoppers.
The recommendation is based on observed shopping patterns rather than a guarantee that the products are necessary for every consumer.
Consumers should therefore determine whether a suggested product actually serves a purpose before adding it to an order.
How Personalization Changes Product Recommendations
Personalization means that a shopping experience can be adapted based on information associated with a particular shopper or shopping session.
AI can make personalization more dynamic by processing multiple signals and adjusting recommendations accordingly.
For example, two consumers visiting the same online store may see different recommendations.
One shopper may have previously shown interest in fitness products, while another may have been researching home office equipment.
The recommendations shown to each person can reflect those different interests.
Personalization can also change during a single shopping session.
If a consumer searches for several products in a particular category, the system may update recommendations based on the new activity.
This can make online shopping more relevant, but it also means that consumers may not experience the same product discovery process as other shoppers.
How Recommendations Work for New Shoppers
Recommendation systems face a challenge when a shopper has little or no previous activity.
This situation is sometimes referred to as a “cold start” problem.
Without a history of searches, views, or purchases, the system has fewer personalized signals to work with.
New shoppers may therefore receive recommendations based on broader information.
These recommendations could be influenced by:
- Popular products
- Current product trends
- Category-level information
- Products similar to the item currently being viewed
- General shopping patterns
As the shopper interacts with the website, the system may have more information to use for personalization.
Consequently, recommendations can become more specific as a consumer searches, views, saves, or purchases products.
Can AI Use Product Reviews?
Product reviews can contain information about how consumers perceive and use products.
Some AI systems may process review-related information when generating recommendations or summaries.
For example, systems can potentially analyze recurring themes in reviews and identify characteristics that appear frequently in customer feedback.
However, reviews should be interpreted carefully.
Individual reviews can reflect different expectations, experiences, and use cases.
A feature that one consumer considers excellent may be irrelevant to another.
AI-generated summaries can also miss context or misunderstand individual comments.
For this reason, consumers may benefit from reading relevant original reviews rather than relying exclusively on an automated summary.
Why Context Matters in AI Recommendations
A recommendation that makes sense in one context may not make sense in another.
Consider a consumer looking at winter clothing.
A recommendation system may suggest products related to cold weather because of the current browsing session.
But if the consumer is actually shopping for someone who lives in a warm climate, the recommendation may not be useful.
AI systems can only make decisions based on the information available to them.
Providing additional context can sometimes improve the relevance of recommendations.
Consumers can use filters, search terms, product preferences, and other available tools to communicate what they actually need.
This is one reason conversational AI shopping assistants can be useful.
Consumers can describe requirements rather than relying entirely on passive behavioral signals.
For more information, see What Are AI Shopping Assistants and How Do They Work?.
Why Two Shoppers May See Different Products
Online shopping does not always produce identical results for every consumer.
Personalized recommendation systems can contribute to these differences.
Two shoppers may receive different recommendations because of differences in:
- Search activity
- Browsing history
- Purchase history
- Product preferences
- Shopping context
- Location-related availability
- Account information
Even without personalization, different recommendations can result from changes in inventory, product availability, promotions, or the timing of a shopping session.
This means a consumer should not necessarily assume that another shopper will see exactly the same products.
The personalized nature of modern e-commerce can create different shopping journeys on the same platform.
Common Types of AI Product Recommendations
AI-powered recommendation systems can appear in several forms.
Recommended for you
These recommendations are typically personalized based on available information about the shopper or shopping session.
Similar products
These suggestions focus on products that share characteristics with the item currently being viewed.
Customers also viewed
These recommendations can be based on browsing patterns involving the same product.
Frequently purchased together
These suggestions identify products that commonly appear together in purchases.
Related products
These recommendations focus on products that are associated with the current product or category.
Alternative products
These suggestions may provide different products that could serve a similar purpose.
Recently viewed products
These are products the shopper has previously viewed and may want to revisit.
Different shopping platforms can use different terminology and recommendation methods.
Limitations of AI Product Recommendations
AI recommendations can be useful, but they are not perfect predictions of what a consumer should purchase.
One limitation is incomplete information.
An AI system may not know why a shopper viewed a particular product or whether a previous purchase was intended for personal use.
Another limitation is changing consumer preferences.
People’s needs can change over time, while recommendation systems may continue to use historical signals.
Recommendations can also be affected by product availability.
A system may identify a relevant product, but that product could become unavailable or change in price.
Other limitations include:
- Incorrect product data
- Outdated information
- Irrelevant recommendations
- Overreliance on past behavior
- Incomplete understanding of personal preferences
- Difficulty interpreting unusual shopping needs
For consumers, the practical lesson is simple: a recommendation is a suggestion, not a requirement.
Can Consumers Control Recommendations?
The amount of control available depends on the shopping platform.
Some websites allow consumers to modify preferences, manage account settings, remove products from recommendation histories, or adjust personalization options.
Other platforms may provide fewer controls.
Consumers can also influence recommendations through active shopping behavior.
Using specific search terms, applying filters, selecting product categories, and providing clear preferences can help communicate what they are looking for.
In some cases, simply starting a new search can produce a different set of results because the shopping context has changed.
Consumers who want to understand how online shopping systems influence what they see can also read How Online Shopping Search Works.
AI Recommendations and Consumer Privacy
Personalized recommendations can involve consumer data, making privacy an important consideration.
Depending on the platform, information related to browsing, searches, purchases, preferences, or account activity may be used to personalize the shopping experience.
Consumers should not assume that every website handles this information in the same way.
Privacy practices can differ between retailers, marketplaces, applications, and AI services.
Consumers can review the privacy information provided by the service to understand what information is collected and how it may be used.
It is also useful to distinguish between information that is necessary for a shopping service and information that is provided voluntarily.
For a broader explanation of online shopping data practices, see Online Shopping Privacy: What Happens to Your Data?.
The Future of AI Product Recommendations
AI-powered product recommendations are likely to become more sophisticated as e-commerce platforms improve their use of artificial intelligence.
Future recommendation systems may become better at understanding the reasons behind a shopping request rather than relying primarily on historical behavior.
Conversational shopping could also become more important.
Instead of passively receiving recommendations, consumers may be able to explain their requirements directly and ask an AI system to adjust suggestions based on those requirements.
For example, a shopper could specify:
- The intended use of a product
- A preferred price range
- Specific features
- Size requirements
- Material preferences
- Compatibility requirements
The system could then use these requirements as additional recommendation signals.
Visual AI may also influence product discovery. Consumers could potentially use images to identify products or find visually similar alternatives.
These developments could make product discovery increasingly interactive.
However, accuracy, transparency, privacy, and consumer control will remain important considerations as recommendation technology develops.
Frequently Asked Questions
How does AI recommend products?
AI recommendation systems analyze available product and shopping information to identify products that may be relevant to a particular consumer or shopping situation.
What information can AI use for product recommendations?
Depending on the platform, AI can use information such as browsing activity, searches, purchases, product attributes, shopping patterns, and stated preferences.
Why do I see different product recommendations than someone else?
Recommendations can vary because shoppers have different browsing histories, purchases, preferences, shopping contexts, and other signals available to the platform.
Does AI know what product I want to buy?
Not necessarily. AI systems estimate relevance based on available information. A recommendation does not mean the system knows a consumer’s intentions with certainty.
Can AI recommend products I have never searched for?
Yes. Recommendation systems can identify products based on relationships with products you have viewed, purchased, or searched for, as well as broader shopping patterns.
What does “Customers Also Viewed” mean?
It generally indicates that other shoppers viewed those products in connection with the product currently being examined.
What does “Frequently Bought Together” mean?
It generally refers to products that appear together in purchases often enough for the shopping platform to identify a relationship.
Are AI product recommendations personalized?
They can be. Some recommendation systems use information associated with an individual shopper or shopping session to personalize the products displayed.
Are AI recommendations always accurate?
No. Recommendations can be irrelevant or inaccurate because the system may have incomplete information, outdated data, or an imperfect understanding of the shopper’s needs.
Can AI recommendations influence what consumers buy?
Recommendations can influence which products consumers notice and consider. However, consumers ultimately decide whether a product fits their needs and whether to purchase it.
Can AI recommendations use product reviews?
Some systems may analyze review-related information, but consumers should still consider the original reviews and product information when evaluating an item.
Can I turn off personalized recommendations?
That depends on the platform. Some services provide personalization controls, while others offer limited options.
Does AI product recommendation affect privacy?
It can. Personalized recommendations may involve information about shopping activity or preferences. Consumers should review the privacy practices of the services they use.
Should consumers rely entirely on AI recommendations?
No. AI recommendations can be useful for discovering and comparing products, but consumers should independently evaluate important specifications, prices, reviews, shipping information, and return policies.
Final Thoughts
Artificial intelligence is becoming an important part of how online shopping platforms help consumers discover products.
Rather than displaying the same products to everyone, modern recommendation systems can analyze product information and shopping signals to identify options that may be relevant to an individual shopper.
Browsing activity, previous purchases, product characteristics, shopping patterns, and stated preferences can all potentially contribute to recommendations.
This can make online shopping more convenient.
A consumer may discover a useful product without knowing the exact search term needed to find it. Related products can also make it easier to explore a category or compare alternatives.
But AI recommendations have limitations.
A recommendation system does not know everything about a consumer. A previous search may have been accidental. A purchase may have been a gift. A product viewed yesterday may no longer be relevant today.
Product information can also change, including price, availability, specifications, and other details.
For these reasons, consumers should treat AI recommendations as a starting point for product discovery rather than as an automatic purchasing decision.
One useful approach is to use recommendations to identify possibilities, then evaluate those products independently.
Checking product specifications, reading relevant reviews, comparing prices, and reviewing shipping and return information can provide additional context.
Privacy is another consideration.
Personalized recommendations can depend on information about consumer activity, so shoppers should understand the data practices of the platforms they use and take advantage of available privacy and personalization controls.
As artificial intelligence continues to develop, product recommendations will likely become more conversational, personalized, and integrated into the broader shopping experience.
The technology can make product discovery faster and more convenient, but the consumer’s own judgment remains an essential part of the process.