How Machine Learning Algorithms Deliver Personalized Shopping Recommendations

Once upon a time, digital shopping wasn’t much different than physical shopping. All your customers were greeted with a one-size-fits-all storefront that featured the same products, the same offers, and the same experiences.

But thanks largely to Amazon and subsequent enterprise retailers like Walmart and Target, the rules have changed. The best ecommerce experiences are now highly personalized, adjusting in real time based on a trove of data that makes shopping uniquely tailored for every customer.

Machine learning, a subset of AI that uses algorithms to identify patterns and make predictions, is the driving force behind this shift. By analyzing hundreds of data signals—like how long a shopper lingers on a product page, the time of day they browse, or the sequence of items they click on, ML-powered ecommerce platforms adapt dynamically.

They adjust to the user, offering products that resonate in the moment, based on all these signals. This is the basic machinery that drives personalized shopping recommendations, a fundamental staple of effective ecommerce experiences.

What Are Machine Learning Algorithms?

Machine learning algorithms are sets of instructions or processes that AI systems use to analyze data, identify patterns, and make predictions or decisions. They do this without being explicitly programmed. ML allows AI systems to learn without human input or interaction.

In ecommerce, these algorithms facilitate personalization. That is, they tailor the various bits and pieces of the shopping journey to the shopper like product recommendations and customized home pages.

ML algorithms make it possible for retailers to transform what was once a static ecommerce experience into a personalized one by recommending relevant products, optimizing pricing, producing meaningful search results, and more.

The experiences get better—more relevant, personalized, and accurate—as the system ingests more data. It’s a virtuous cycle that drives more sales (happy retailers) and creates better experiences (happy customers).

Types of Ecommerce Machine Learning Algorithms

Machine learning encompasses a wide range of algorithms, each designed to solve specific problems or analyze data in unique ways. While the algorithms listed below are some of the most used in ecommerce, this is by no means an exhaustive list.

1. Collaborative Filtering

Collaborative filtering works by analyzing user behavior to predict preferences. User-based filtering finds users with similar preferences and recommends products they’ve liked. Item-based filtering identifies relationships between products based on user interactions. For example, if many customers buy a phone and a specific case together, the system will recommend the case to future phone buyers.

2. Content-Based Filtering

This approach focuses on the attributes of products and user preferences. By creating a profile for each user and product, the algorithm matches users with items that share similar characteristics to those they’ve interacted with before. A shopper who frequently buys organic skincare products will get recommendations for items with similar ingredients, for example.

3. Hybrid Models

Hybrid models combine collaborative and content-based filtering, so shoppers get recommendations that use both. Monetate uses collaborative filtering to identify general patterns and refine recommendations with content-based methods for greater relevance.

4. Deep Learning Models

Deep learning algorithms (e.g., neural networks, general adversarial networks, recurrent neural networks, etc.) are algorithms that attempt to mimic the way the human brain works. They process complex, unstructured data like images and text. For example, a deep learning model might recommend clothing based on visual similarities, such as suggesting a pair of bootcut jeans after analyzing the style and cut of jeans a shopper has browsed.

5. Matrix Factorization

Matrix factorization is a technique that breaks down large datasets of user-item interactions into smaller, dense matrices. This method uncovers latent features, like a user’s affinity for specific product categories, enabling more accurate predictions. Netflix uses matrix factorization to recommend movies by identifying hidden patterns in user ratings and viewing history. In ecommerce, this could look like a platform recommending a specific brand of running shoes to a user who has shown interest in both athletic gear and high-performance footwear, even if they haven’t browsed that exact product before.

The Need for Personalization in Online Shopping

Where once personalization was a nice-to-have feature, it’s now a fundamental customer expectation. McKinsey has long reported that three-quarters of consumers want—and expect—personalization. Without it, people become frustrated. It’s a feature shoppers associate with positive experiences, one that makes them feel seen.

Companies are learning that personalization, done well, directly benefits their bottom line. Another study by McKinsey revealed that personalization lifts revenue 5 to 15% and can increase marketing ROI by as much as 50%. When retailers get personalization right, they see a 10-15% boost in sales conversion rates and a 20% increase in customer satisfaction.

This is how personalization translates to real business impact. Now that your head is buzzing with stats, we’ll end with a simple, telling fact. Static, one-size-fits-all shopping experiences no longer cut it in a world where consumers know what’s possible with personalization.

Key Machine Learning Techniques Used in Product Recommendations

Personalization engines like Monetate collect and process data from multiple sources then use machine learning to identify patterns. The information collected includes (but isn’t limited to), browsing behavior, purchase history, real-time user actions, and customer attributes like demographics and location.

Our system uses this information to learn the specific user’s shopping patterns—what they like and don’t like—then applies the following ML techniques to provide tailored product recommendations:

•   Affinity Scoring: Assigns scores to products based on user preferences, such as color, size, or brand.

•   Dynamic Filtering: Uses behavioral data to fine-tune recommendations in real-time.

•   Pattern Recognition: Identifies patterns in customer behavior to predict preferences and intent.

•   Algorithmic Models: Includes top-selling products, recently viewed items, and geo-targeted recommendations.

Based on the above techniques, our system adjusts a given experience in real time, customizing how a product listing page (PLP) appears, for example, or prioritizing items in search results so that the most relevant products (based on the context of the search and searcher) appear at the top.

Real-World Examples of Personalized Shopping Recommendations

Here are two examples of companies that are incredibly effective at using machine learning to create personalized shopping recommendations.

1. Amazon: Navigating a Massive Catalog

Amazon uses AI and machine learning to personalize product recommendations on their home page, category pages, and throughout the entire website. With over 300 million products, the technology is essential to match the right products to the right customers.

Amazon recently announced that they were pairing their recommendations engine with generative AI to provide more specific recommendations (e.g., instead of “more like this” a shopper might see “Gift boxes in time for Mother’s Day”). The system analyzes product attributes along with customer data and browsing history to produce these highly relevant and personalized recommendations.

2. Walmart: Inventory Optimization Meets Customer Satisfaction

Walmart has 255 million weekly customers across 10,500+ stores in 19 countries. Considering the staggering scale of their operation, a static website is simply not an option. And, in fact, Walmart (like Amazon) is a leader in using machine learning and AI to personalize the entire shopping journey.

Their latest initiative, the Content Decision Platform, is an AI-powered system that analyzes customer behavior and preferences to tailor content like the homepage, predicting and displaying which products will be most relevant for each shopper.

Their newest initiative, Content Decision Platform, aims to hyper-personalize shopping experiences by using a combination of GenAI, predictive analytics, and machine learning to deliver individualized content like relevant products on a customer’s home page. From Walmart’s website, “With this technology, Walmart will create a unique homepage for each shopper making the online shopping experience as personalized as stepping into a store designed exclusively for each customer.”

The Future of Machine Learning in Ecommerce

We’ve already entered the era of hyper-personalized product recommendations and shopping experiences. What could possibly be next? Emerging trends like deep learning and predictive analytics will create even more relevant and customized  ecommerce experiences. Looking forward, here are some things we can anticipate

1. Next-Generation Personalization

Rather than just adapting to what shoppers have done before, AI systems will create truly dynamic experiences that evolve in real-time across every touchpoint. This means your entire shopping journey – from search to checkout – will automatically adjust based on your current context, preferences, and needs. Think of it as a leveling up of what’s already a very relevant and personalized piece of the digital shopping journey, so a feature like personalized recommendations becomes even more effective.

2. Better Predictions

Advanced machine learning will become much better at using predictive analytics to anticipate customer behavior. These systems will understand purchase cycles and patterns so well that they can proactively serve up relevant products and content before shoppers even know they need them. This predictive capability will be especially powerful for repeat purchases and complementary items.

3. Contextually Aware Shopping

Ecommerce systems will get better at understanding when and how to engage customers. Using AI, these systems will consider factors like time of day, season, location, and weather to determine what, when, and how to recommend items to shoppers. Think maximum relevance, minimal friction.

4. Emotional Intelligence

The next frontier in ML is all about emotional intelligence. AI systems will become better at analyzing subtle behavioral signals and interaction patterns to decode and detect customer sentiment. They’ll adjust experiences accordingly, for example, by offering help when someone seems frustrated or celebrating with them during positive moments.

While it sounds revolutionary (particularly the last two predictions), we think the next phase of machine learning will be a bit less Star Trekian, but no less impactful. It will see highly effective ML-powered personalization become the norm across all ecommerce experiences. Systems will become more predictive, context-aware, and emotionally intelligent in how they engage with shoppers.