{"id":22829,"date":"2024-01-23T20:06:23","date_gmt":"2023-11-24T16:23:00","guid":{"rendered":"https:\/\/www.bloomreach.com\/library\/the-value-of-personalized-product-recommendations-in-ecommerce"},"modified":"2026-06-08T16:06:00","modified_gmt":"2026-06-08T16:06:00","slug":"ecommerce-product-recommendation-engine","status":"publish","type":"library","link":"https:\/\/www.bloomreach.com\/en\/blog\/ecommerce-product-recommendation-engine","title":{"rendered":"What Is a Product Recommendation Engine? (And How to Use One in Ecommerce)"},"content":{"rendered":"

Matching the right customers with the perfect products is an essential task for any ecommerce business, which is why product recommendations are so important for success.<\/span><\/p>\n

But recommending items online isn’t a simple job. While customers can get tailored recommendations from a sales rep in brick-and-mortar stores, ecommerce brands need to build recommendation systems that determine which popular products are right for their particular audience.<\/span><\/p>\n

According to <\/span>Salesforce research<\/span><\/a>, visits where shoppers click a product recommendation make up just 7% of all traffic \u2014 but those visits generate 26% of revenue. That kind of asymmetric return is why recommendation engines have become one of the highest-value investments in any serious ecommerce program.<\/span><\/p>\n

With so much riding on the success of your ecommerce site’s recommendations, it’s worth diving into the hows and whys behind product recommendation engines. Keep reading to learn what a product recommendation engine is, how modern AI approaches like real-time personalization and neural networks have raised the bar for personalization, and where to deploy recommendations at every stage of the customer journey.<\/span><\/p>\n

What Are Product Recommendations in Ecommerce?<\/b><\/h2>\n

Ecommerce product recommendations are products that online brands offer their customers, drawing on data about views, sales, and reviews to choose products their customers will likely enjoy.<\/span><\/p>\n

These recommendations mirror the tailored in-store experience that shoppers would have with a salesperson, who can learn about the customer’s interests, intents, and needs through live conversation. They can make a real connection with a consumer and then make recommendations from that interaction.<\/span><\/p>\n

Ecommerce product recommendations provide your business with the opportunity to have these types of interactions with your customers throughout their online shopping journey.<\/span><\/p>\n

Using the digital touchpoints your audience has with your brand , like your ecommerce store, your mobile app, or <\/span>an email campaign<\/span><\/a> , you can provide relevant suggestions for products and inspire customers to make additional purchases.<\/span><\/p>\n

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\"Query<\/p>\n

What Is an Ecommerce Product Recommendation Engine?<\/b><\/h2>\n

An <\/span>ecommerce product recommendation engine<\/span><\/a> is an algorithm that determines which products to recommend to customers by filtering and sorting your online store’s items based on a set of rules. This process uses data about your products, such as the number of views, sales, or even reviews, to present the right items to the right audience.<\/span><\/p>\n

While straightforward recommendations can be created using predetermined recommendation models (like “frequently purchased,” “best selling products,” and “past purchases”), modern <\/span>personalization engines<\/span><\/a> utilize advanced AI technologies, including <\/span>natural language processing<\/span><\/a> and machine learning algorithms, to personalize recommendations to each individual customer across multiple touchpoints in the customer experience. Today’s most advanced engines go further still, using real-time behavioral signals and deep learning architectures to generate recommendations in milliseconds , before a shopper has even finished browsing.<\/span><\/p>\n

\"Frequently<\/p>\n

The presentation of these results can be as simple as a product highlight in a social ad or email campaign, or you can use them to order products that appear on your ecommerce website’s homepage or category pages. You can use them to influence buyers at every stage of the <\/span>customer journey<\/span><\/a>.<\/span><\/p>\n

Types of Recommendation Engines<\/b><\/h3>\n

There are several types of recommendation engines, each employing different methodologies to generate relevant recommendations:<\/span><\/p>\n

Collaborative Filtering<\/b><\/h4>\n

Collaborative filtering is one of the most popular methods used in recommendation engines. It operates by finding patterns in user behavior and suggesting products that similar users have liked.<\/span><\/p>\n

This method is particularly effective because it draws on the collective wisdom of the crowd to make suggestions based on the preferences of users with similar tastes. For instance, if two users have purchased similar items in the past, the system might recommend a product liked by one user to the other.<\/span><\/p>\n

Content-Based Filtering<\/b><\/h4>\n

Unlike collaborative filtering, content-based filtering focuses on the attributes of the products themselves. This method analyzes a customer’s past interactions with products to identify common features or characteristics they prefer. The system then recommends products that match these identified traits.<\/span><\/p>\n

Content-based filtering is beneficial for users with unique tastes, as it tailors suggestions based on individual preferences rather than relying on the behavior of others.<\/span><\/p>\n

Hybrid Models<\/b><\/h4>\n

Hybrid models combine collaborative and content-based filtering to provide more comprehensive recommendations.<\/span><\/p>\n

By integrating multiple data sources and methodologies, hybrid models can overcome the limitations of individual approaches, resulting in more accurate and personalized suggestions. This synergy of techniques offers a balanced approach, maximizing the strengths of each method to improve the quality of recommendations.<\/span><\/p>\n

For a deeper technical look at how these approaches compare, Google’s machine learning documentation offers a useful <\/span>overview of recommendation system types<\/span><\/a>.<\/span><\/p>\n

For more on how trending product signals feed into these models, see Bloomreach’s guide to <\/span>trending product recommendations<\/span><\/a>.<\/span><\/p>\n

How Modern AI Product Recommendation Engines Work<\/b><\/h2>\n

The recommendation engines described above (collaborative filtering, content-based filtering, and hybrid models) represent the foundational layer of how personalization works. But the gap between a basic recommendation engine and a modern AI-powered one is significant, and understanding that gap matters when you’re evaluating tools or trying to explain a budget request internally.<\/span><\/p>\n

From Batch Processing to Real Time<\/b><\/h3>\n

Traditional recommendation engines ran on a batch processing model. The system would ingest data once a day (or once a week), retrain or update its models overnight, and serve recommendations based on what customers did yesterday. That’s a bit like briefing a salesperson using last week’s call notes and hoping those notes still apply to the customer walking in right now.<\/span><\/p>\n

Modern engines process behavioral signals continuously. A customer clicks on a pair of trail running shoes, spends 45 seconds on the product page, adds a second pair to their cart for comparison, then navigates back to the category page. A real-time recommendation engine picks up each of those signals as they happen and updates what it shows that customer within milliseconds. By the time they land on the category page, the recommendations already reflect their demonstrated interest, not their purchase history from three months ago. The shift from batch to real-time processing is typically the most significant factor in AI recommendation performance. For more, see <\/span>why ecommerce businesses need AI recommendations<\/span><\/a>.<\/span><\/p>\n

How Neural Networks Power Modern Recommendations<\/b><\/h3>\n

Under the hood, many modern recommendation engines use deep learning architectures to solve a problem that simpler models struggle with: what do you recommend to a customer you’ve never seen before?<\/span><\/p>\n

One widely used approach is the two-tower model. The architecture creates two separate mathematical representations: one for the user (incorporating session behavior, device context, and any available history) and one for each product in the catalog (incorporating attributes, purchase patterns, and content signals). The engine then finds the closest matches between them in a shared mathematical space, surfacing products that fit the customer’s current context even when purchase history is thin or nonexistent. Bloomreach’s overview of <\/span>two-tower neural networks<\/span><\/a> covers the technical mechanics in more detail if you want to go deeper.<\/span><\/p>\n

Recommendations Across Every Channel<\/b><\/h3>\n

The other meaningful shift in modern recommendation engines is where they operate. A legacy on-site widget shows recommendations on your product pages. A modern engine feeds the same real-time behavioral data into recommendations across your website, email campaigns, push notifications, SMS, and in-app messages simultaneously. A shopper who browsed camping gear on mobile on Tuesday gets a targeted product recommendation in Wednesday morning’s email, because the engine tracks context across all channels, including sessions that never touched the website.<\/span><\/p>\n

This cross-channel capability is what distinguishes a recommendation engine from a simple on-site widget. For a closer look at how this plays out in practice, Bloomreach’s content on <\/span>experience-driven recommendations<\/span><\/a> covers the channel-by-channel deployment in detail.<\/span><\/p>\n

The Benefits of Personalized Product Recommendations<\/b><\/h2>\n

Recommendation engines benefit both sides of the transaction: customers find products worth buying faster, and retailers see higher conversion rates, larger order values, and stronger retention. Here are the main benefits you’ll see when your business utilizes an ecommerce product recommendation engine:<\/span><\/p>\n