How to Build an AI-Powered Recommendation System
in 6 Steps (With Real Examples)

From suggesting the next best movie on Netflix to curating personalized shopping experiences on e-commerce sites, AI-based recommendation systems influence billions of consumer choices every day. For businesses, this presents a tremendous opportunity. For instance, about 35% of Amazon’s sales are attributed to its AI recommendation engine, making it one of the key contributors to the company’s success.

The good news is that you can build a similar system for your own business, whether it’s retail, digital entertainment, or any other sector with substantial customer data.

This article explains the development process step-by-step, from defining business objectives and selecting the right algorithms to deployment and monitoring. We also highlight real-world examples of companies already benefiting from AI-driven personalized recommendations.

How to build an AI recommendation system: Mini-checklist

Define the business goals and use cases.

Plan the technical approach.

Collect and prepare the data.

Build the recommendation engine.

Train and test the model.

Deploy and monitor the system.

An artificial intelligence recommendation engine is a system that provides tailored suggestions for content, products, or services based on user preferences and behaviors.

Here’s how it works: a system uses machine learning algorithms to identify patterns in a large set of customer data. Based on those patterns, it predicts what a user is most likely to enjoy or find valuable. The system then delivers the recommendations to users in real-time as they interact with a platform (an e-commerce store, social media app, video streaming services, etc.)

AI-powered recommendation engines are seeing rapid adoption across various industries. The global market for these systems was valued at $2.8 billion in 2023 and is projected to reach $34.4 billion by 2033. It’s easy to see why: an IBM survey found that two-thirds of consumers discover new products online.

Business benefits of AI-based recommendations

A personalized product recommendation engine delivers mutual value for customers and businesses. Customers no longer need to sift through countless irrelevant options to find what they’re looking for. Meanwhile, companies can improve key performance metrics, achieving:

• Improved conversion rates. AI recommendation systems show options customers are likely interested in, such as movies they’ll enjoy or clothing they’ll buy, leading to higher conversion rates.
• Increased cross-selling. These systems also suggest complementary items based on the customer’s current selection, like matching socks when someone buys shoes, prompting customers to make additional purchases.
• Enhanced user engagement. Timely and contextual product and content recommendations encourage users to linger on a platform and come back for more.
• Data-driven decision-making. Insights gathered by AI-based recommendation systems can inform strategies across various business functions: marketing, inventory management, and product development, resulting in better company performance overall.
• Competitive edge. By offering highly personalized customer experiences, businesses can differentiate themselves in the market, surpassing competitors and increasing their share.

Challenges with AI-based recommendations

Recommendation systems using AI are powerful tools for driving better business results and customer satisfaction. But what are the challenges of building an AI recommendation system? Here are some of the most critical ones:

• Risk of bias. AI recommendation engines rely on the quality of the data they use. If the data is compromised (inaccurate, incomplete), the recommendations can be flawed. This leads to irrelevant suggestions and a poor customer experience.
• Cold start problem. When a new user joins a platform or a company introduces a new item (product, movie, song, etc.), the system will lack sufficient data to analyze and struggle to provide relevant recommendations. This is known as the cold start problem in recommendation systems.
• Overfitting. When an AI recommendation engine is trained on limited data that includes outliers or rare interactions, it causes the model to focus too heavily on those details instead of learning general patterns. For example, if a user watches and rates only action movies, the system might overfit to this behavior and suggest only this genre, even though the user might enjoy other genres as well.

Although these concerns require caution, you can effectively tackle them with the right expertise. An experienced development team handling your AI recommendation system project will help you avoid pitfalls and efficiently overcome technical challenges.

When designing AI recommendation systems, there’s no one-size-fits-all solution. For instance, choosing between collaborative filtering vs. content-based filtering depends on your data and goals. Collaborative filtering works best when user behavior is plentiful and overlapping, while content-based filtering is better suited for cold start scenarios or highly niche content.

User-based collaborative filtering

With user-based collaborative filtering, AI-powered recommendation systems predict what a user might like from the preferences of similar users. Essentially, if user A likes a product and User B has similar tastes, the system will recommend the same product to user B. 

How does it know if users have similar tastes? Technically speaking, developers implement this approach by creating a user-item interaction matrix—a table that tracks users, items (like products or movies), and interactions (such as ratings, clicks, or purchases). The AI recommendation engine then calculates the similarity between a target user and others to provide suggestions.

In user-based collaborative filtering, developers often apply matrix factorization—a technique that decomposes a large interaction matrix into smaller, more manageable matrices. This enables the system to identify latent factors (hidden patterns) that explain the relationships between users and items.

Item-based collaborative filtering

Like user-based filtering, item-based collaborative filtering relies on an interaction matrix. However, instead of comparing users, it compares items based on how they are interacted with by users. The AI recommendation engine then calculates the similarity between items based on these interactions.

For example, if a customer buys a camera, the system might suggest accessories like a tripod, lens, or memory card. It determines these recommendations based on how many users who bought the camera also bought those accessories. The system then identifies the accessories most often purchased alongside the camera and suggests them to other customers buying cameras.

Matrix factorization can also be applied here to reduce the dimensionality of the matrix and uncover hidden patterns in item-user interactions.

Content-based filtering

AI-based recommendation systems using content-based filtering focus on attributes of the content a user interacts with. For example, if a user listens to a lot of indie rock music, the system will recommend other indie rock bands and songs. Unlike collaborative filtering, this method doesn’t consider the behavior of other users.

Content-based recommendation systems work as follows: first, the engine identifies key product attributes (e.g., genre, director, actors, release year for movies). Then, it tracks the user’s interactions, like what they’ve viewed or rated. Finally, the system compares the user’s preferred items to other items in the database and recommends similar ones.

Hybrid approach

How do hybrid recommendation systems work? A hybrid system integrates multiple methods for more accurate recommendations. For example, an AI recommendation engine in a video streaming service might use collaborative filtering to suggest films based on what similar users liked and content-based filtering to recommend movies based on genre, director, or actors that a specific user interacted with.

Although hybrid AI recommendation engines are more complex to build and maintain than the others, they are also more effective. These systems help address limitations of the individual methods, such as the cold start problem in recommendation systems, and provide a perfect balance between personalization (tailoring suggestions for an individual user) and exploration (recommending new or diverse items).

Key types of AI recommendation systems

What are the steps to build an AI recommendation system from scratch? Glad you asked.

Creating a successful AI-powered recommendation system isn’t just about choosing the right algorithms. The process requires a clear product vision and careful technical execution. Here’s an overview of the key steps.

Step 1. Define your business goals and use cases

As the first step, you need to define what type of AI recommendation system you need (i.e., what it will suggest to users) and the goals you aim to achieve (e.g., increase sales, improve retention). While the system will bring multiple benefits, it’s important to focus on your most critical goals, as they dictate the features, algorithms, and data you’ll need.

For example
If your primary goal is to encourage customers to buy more, the recommendation system might need to prioritize upselling and cross-selling. This means you’d need to collect data about purchase patterns, price sensitivity, and product relationships. The most suitable recommendation system type might be item-based collaborative filtering or a hybrid approach.

Step 2. Translate your vision into technical terms

After you’ve defined your business goals and use cases, you need to outline the technical implementation details:

• Choose a technology stack. The stack will depend on your AI recommendation system’s needs, existing infrastructure, and scalability requirements. Common choices include Python for programming, Apache Mahout or Surprise library for recommendation engines, MongoDB for flexible data storage, and AWS, Google Cloud, or Microsoft Azure for cloud infrastructure.
• Select the right algorithms. There’s no one answer to the question, “What algorithm is best for product recommendations?” The best algorithm depends on your data and goals. Collaborative filtering works well when user behavior data is abundant, while content-based filtering excels when item attributes are more informative. A hybrid approach often delivers the most accurate recommendations in complex scenarios.
• Design the architecture. The AI recommendation system architecture defines how its components (data pipelines, APIs, storage, and processing layers) interact to deliver relevant suggestions at scale.
• Outline key functionalities. Besides the recommendation engine itself, additional essential functions of the recommendation system include user profile management, content/item profiling, and feedback loops for continuous learning.

At this stage, it’s crucial to involve technical experts who can suggest the necessary technologies and features and guide development decisions for your specific case. For example, with generative AI, recommendation systems can go beyond traditional capabilities by creating personalized content, providing dynamic suggestions, and simulating user feedback for new items.

Step 3. Gather and prepare your data

The quality, quantity, and structure of the customer data you use impact the accuracy and effectiveness of your AI-based product recommendation system. To ensure that your data is ready for model training, you need to follow multiple steps:

• Data collection. This involves tracking user interactions, such as what they view, click, purchase, or rate, along with product or content details (like category, price, or brand). Gathering external data like social media activity, market trends, and demographic information can also be useful.
• Data augmentation (optional). When there’s insufficient data from actual users, you can use synthetic data (artificially generated data that mimics real-world data).
• Data cleaning and standardization. This includes handling missing values, removing duplicates, and scaling numerical data to ensure consistency and prevent bias in the AI model.
• Feature engineering. At this point, you need to identify and create meaningful features from raw data. For example, from user behavior data, you might derive features such as frequency of purchases, average rating, or time spent on items.

With your data prepared, the next step is building the AI recommendation engine so you can put everything into action.

Step 4. Build the AI recommendation engine

That’s when you code the selected recommendation algorithms into the backend and set up the system’s architecture.

You’ll also need to integrate data pipelines to ensure the system can access and process data that will evolve over time. You can implement either a real-time recommender system that generates suggestions instantly as users interact with the platform, or use batch processing for periodic updates.

These steps essentially prepare the environment and technical components that enable the model (see Step 5) to function properly.

Step 5. Train and test the model

Training means feeding cleaned, prepared, and feature-engineered data to the algorithm. During this phase, the algorithm detects patterns and relationships in the data, effectively creating the model. The model can then use the patterns it learns to make accurate predictions and recommendations.

After model training, it’s crucial to evaluate its performance using test data (data that the model has never seen before). This step ensures that the model generalizes well and doesn’t simply memorize the training data (i.e., overfit the data). By testing the model on fresh data, you can identify and address areas for improvement.

Step 6. Deploy and monitor the system

The next step is to deploy the AI recommendation system into production, making it live and ready to serve users. You’ll also need to integrate the system into your platform (a website, mobile app, or another interface), ensuring the recommendations are delivered correctly within real-life user interactions.

However, deployment isn’t the final step. You must continuously monitor the system to ensure it performs optimally, even as user behavior and trends evolve. Over time, models can drift, meaning the system’s performance can decline as data changes. If this happens, you’ll need to retrain the model with fresh data or fine-tune its parameters.

How to build an AI recommendation system: Checklist

➤ Define the business goals and use cases

• Define the primary business objective (e.g., increasing upselling, improving retention).
• Specify what the system will recommend (e.g., products, content, services).
• Select the appropriate recommendation type based on the goal.

➤ Plan the technical approach

• Choose a technology stack (e.g., Python, MongoDB, a cloud provider).
• Select the most suitable algorithm: collaborative filtering, content-based filtering, or a hybrid method.
• Design the system architecture, including data pipelines, APIs, and storage.
• Define the core system features, such as user profile management and feedback loops.
• Consider using generative AI if the system needs to generate personalized content or simulate user behavior.

➤ Collect and prepare the data

• Collect user interaction data (views, clicks, purchases, ratings) and item metadata (category, price, brand, etc.).
• (Optional) Incorporate synthetic or external data to enrich the dataset.
• Clean the data by handling missing values, removing duplicates, and standardizing formats.
• Perform feature engineering to extract meaningful variables from the raw data.

➤ Build the recommendation engine

• Implement the selected algorithms and the supporting backend logic.
• Set up data pipelines for real-time or batch processing of new data.
• Integrate all system components to ensure smooth operation.

➤ Train and test the model

• Train the model using the prepared and engineered dataset.
• Test the model on a separate dataset to evaluate performance and prevent overfitting.
• Analyze the model’s accuracy and adjust as needed.

➤Deploy and monitor the system

• Deploy the system into production and integrate it with the user interface.
• Monitor the system’s performance over time to ensure relevance and accuracy.
• Retrain or fine-tune the model periodically to address changes in user behavior or data quality.

Businesses in all sectors use AI to deliver personalized suggestions that drive user engagement, increase sales, and improve customer loyalty. Just take a look at these successful AI recommendation system examples.

Amazon

Amazon was one of the first e-commerce businesses to successfully implement a product recommendation system using AI. By analyzing customers’ shopping activity, such as browsing history, search queries, and purchasing patterns, the system generates personalized deals and product suggestions on the homepage. But that’s not all.

Leveraging GenAI, the recommendation system used by Amazon tailors the wording of product descriptions to align with customer search terms. For example, if a shopper searches for “vegan makeup products,” the system ensures that the term “vegan” is prominently displayed in the description of suggested items, even if it’s buried at the end in the original text.

Netflix

Netflix uses a hybrid approach to provide users with real-time recommendations for shows, movies, and games. Its machine learning models estimate the likelihood of a user appreciating a particular title by analyzing their interactions, preferences of similar users, details of each item (such as genre, category, and actors), and additional factors like time of day and viewing duration.

What’s more, Netflix enhances its AI-based recommendations with other personalization features. For instance, it changes the arrangement and prioritization of content rows, like “Continue Watching” and “New on Netflix,” for each user, starting each row with titles most likely to appeal to the specific user.

Meta (Facebook)

Without personalization, social media users can quickly feel overwhelmed and lose interest in content. Meta (Facebook) uses an advanced recommendation system with over 100 artificial intelligence models to prevent this from happening.

Here’s how it works: first, the system gathers all possible posts a user might see, including those from friends and groups. Then, it analyzes various signals for each post, like user interactions and content details. Next, it predicts how likely a user is to engage with a post—whether they’ll like, share, or comment on it. Finally, the system calculates a relevance score for each post based on the predictions it made and orders them accordingly.

This approach ensures users only see the most relevant content, while still discovering new posts.

AI recommendation systems allow businesses to connect with customers in more meaningful ways. By analyzing vast amounts of user data, they provide accurate personalized product and content suggestions. As a result, users save time and enjoy a tailored customer experience without having to sift through irrelevant options. For companies, this leads to increased sales and stronger loyalty.

At DevCom, we offer a range of artificial intelligence development services that help businesses around the world grow through innovation. Our team builds robust AI-powered recommendation systems that deliver measurable results. Contact us today to discuss your project!