Recommendation systems and machine learning: approaches and case studies

Recommendation systems and machine learning: approaches and case studies

September 26, 2023

Aleksandr Ahramovich
by Aleksandr Ahramovich, Head of AI/ML Center of Excellence
Machine learning-based recommendation systems are powerful engines using machine learning (ML) algorithms to segment customers based on user data and behavioral patterns (such as purchase and browsing history, likes, or reviews) and target them with personalized product or content suggestions.

Let's explore these tools’ nature and different design approaches and find out how machine learning experts can help various businesses adopt recommendation engines to boost customer engagement and improve user experience.

Approaches to recommendation system design

Most recommendation engines fall into three major sub-categories depending on the approach used to select and recommend products or services.

Comprehensive purchase pattern interpretationProblems scalability and computational power Collaborative filteringReduces new products’ cold start issueRequires accurate product catalogingContent-based filteringMitigates the respective drawbacksMore complex architectureHybrid approach

Collaborative filtering: a focus on user similarity

How it works

Recommendation engines in this category rely on machine learning algorithms, such as clustering models, user-based k-nearest neighbors, matrix factorization, and Bayesian networks, to survey customers’ perceptions of products. After identifying customer preferences, an engine will offer items already bought by other users with similar tastes. The system can define users via explicit data collection (asking them to compile a list of favorite items and rate previously purchased products) or implicit data collection (scanning user interactions on social media powered by AI as well or monitoring purchases and browsing on ecommerce websites).

Collaborative filtering

LeopoldBought by both usersSimilar usersBought by , recommended to LeopoldJudeJude

Collaborative filtering systems can be extremely accurate and provide effective suggestions, especially when relying on context-aware filtering.

These systems can predict customers' interest in a product they didn't know existed by observing what caught the attention of similar users.

Recommendation engines based on this approach perform well even without understanding the nature of each item, which eliminates the need for detailed product descriptions. 


Cold start problem: providing valuable suggestions to new users with no purchase history can be challenging considering the only available parameters (gender, age, etc.).

Scalability: using this algorithm to search for purchase patterns among a growing number of customers and products requires significant computational power.

Rich-get-richer effect: algorithms generally recommend products with many excellent reviews, increasing their popularity at the expense of new items.

Data sparsity: in cases with a large product catalog, each item may not have a sufficient number of user reviews to analyze, reducing the recommendations' accuracy.

Shilling attacks: new products are vulnerable to rating manipulations (such as negative reviews from competitors).

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How ML-powered recommendation systems work

Whether based on collaborative or content-based filtering, ML-powered recommendation systems will follow a multi-stage pipeline to turn product and customer data into personalized suggestions.

Data collection and segmentation

A machine learning system needs large data sets to segment customers, namely categorize them into a certain archetype or buyer persona according to their attributes, and target them with suitable suggestions. Relevant metrics can include browsing behavior, purchase history, content usage, personal information from user profiles, product reviews, access devices, and many more. The system can gather this information via explicit or implicit data collection, while product features can be obtained from the related tags.
BehavioralPurchase patternsUsage rateLoyalty statusDemographicAgeIncomeGenderPsychographicPersonalityLife styleInterestsGeographicRegionCity sizeClimate

Top recommendation systems

Nowadays, all major digital service providers and ecommerce enterprises rely on recommendation systems in a variety of use cases to deliver a customized user experience and enhance sales performance or advertising revenues.

Amazon leverages a recommendation algorithm for products or search results, combining in-site suggestions based on several strategies (recommended for you, bought together, recently viewed, etc.) with off-site recommendations via email. The ecommerce leader deployed its collaborative filtering-based recommender engine between 2011 and 2012, recording an outstanding 29% sales increase in the second fiscal quarter of 2012. However, Amazon is now shifting from item-based collaborative filtering (users who bought A also bought B) to a system based on autoencoder neural networks.

ML-based recommender engines implementation tips

ML-based recommender engines are complex tools with intricate architectures, so you can’t reap their benefits without proper implementation. Here are some factors to keep in mind:


Collaborative, content-based, and hybrid approaches determine recommendation systems' underlying mechanics and the variables to prioritize. The design choice depends on your business scenario, including your target audience and product range, as well as the data available. For example, major retail platforms with a broad service or product offering can find the hybrid approach to achieve high performance and address the wide range of tasks they have.

User-driven strategies

The most effective recommender systems can switch between different recommendation strategies depending on the customer journey stage. For example, first-time users could be directed towards the "most popular" products, as we still can't frame their interests, while visitors with a long purchase history can be targeted with tailored recommendations based on user affinity with certain categories of merchandise.

Page context-driven strategies

Different page contexts can also call for specific recommendation strategies. For instance, a standard choice for home pages is the “most popular” strategy, while recommendations based on “similar products” can work better for product pages. Regarding cart pages, it's worth embracing a “bought together” strategy to encourage upselling.

Platform-based solutions

Nothing prevents you from building recommender systems fully tailored to your needs if you're ready to face higher upfront costs for its development. However, you can also rely on cloud-based services and tools providing built-in algorithms and pre-trained ML models. These include Salesforce Interaction Studio, Adobe Target, Amazon Personalize, Optimizely, IBM Watson Real-Time Personalization, and more.

ML-based recommendation system benefits

Retail companies and digital service providers are relying on machine learning-powered recommendation systems and other big data ecommerce solutions to achieve six essential goals:

Better user experience

Recommendation systems help replicate in-store customer care and personalized guidance offered by a real salesperson in a virtual environment.

Focus on the right product

Recommender systems mitigate the so-called information overload as they direct customers towards the product they want, hidden amid an overwhelming range of merchandise and content.

Sales and revenue drive

Personalizing shopping experience and highlighting relevant products result in a higher number of items per order, superior average order value, enhanced customer retention and lifetime value, and, therefore, revenue growth.

Data-driven decision-making

Recommendation systems gather customer and sales data which can be used to compile detailed reports, providing managers with valuable insights into customers’ preferences and enhancing decision-making for marketing, logistics, and pricing strategies.

Optimized marketing costs

Along with targeted advertising and triggered communications, recommendation systems can help mitigate marketing costs. In this regard, McKinsey highlighted that product recommendations can improve marketing-spend efficiency by 10-30%.

Driving personalization with machine learning

Driving personalization with machine learning

Recommendation systems offer a synthesis between customers' wishes for a customized user experience and personalized digital services and the need for retail companies and digital service providers to improve their sales performance. Machine learning in ecommerce and retail, as well as HR and eLearning has further fostered this synergy, allowing enterprises to fully personalize customer journeys.

On the flip side, these data-driven technologies are computationally demanding and can create tension between sheer performance and the growing demand for privacy and data protection from both the public and legislators. To streamline their implementation and ensure compliance, rely on an expert partner like Itransition.

Driving personalization with machine learning

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