These days, users expect every click and scroll to feel personal. They look for apps, news sites, social networks, and online stores that remember their tastes and recommend content that truly matters. Netflix set the pace early on; its recommendation system helps the company save $1 billion annually under the indirect cost savings label and win customer trust. Of course, more and more companies, regardless of their field, seek to learn how to build a recommendation system that mirrors such success.
I am Oleh Komenchuk, an ML Department Lead at Uptech. In this article, I share my insights on recommendation systems. I will explain what they are, describe their types, outline the steps to build one, and discuss best practices. Join me on this journey as we explore the art and science of building a recommendation engine that speaks directly to users.
What is a Recommendation System?
A recommendation system, often referred to as a recommender engine, is a type of software that applies machine learning algorithms and information filtering methods to predict user preferences and provide targeted suggestions. Its main goal is to study historical data — such as ratings and past interactions — to match users with items available in the system. These items can include anything from content (e.g., movies, music) to products (e.g., books, electronics, clothes) and more.
An ML-powered recommendation engine processes large amounts of user data to determine patterns and similarities to make the most relevant predictions of what a certain user might like.
For instance, Netflix suggests movies based on your viewing history, Amazon proposes products informed by previous purchases and browsing behavior, and Spotify crafts playlists using listening patterns. By using techniques such as clustering, collaborative filtering, and deep neural networks, recommendation systems continuously refine their suggestions to suit each user's tastes and needs.
6 Steps to Build a Recommendation System with ML
Below, we’re sharing the main steps you will need to take in order to build a recommendation system using machine learning.
The process generally includes 6 stages, during which you, together with the ML and data science team, do the following:
- Perform discovery and set goals
- Conduct data collection and preparation
- Choose the approach to create a recommendation system
- Train and test the ML recommendation model
- Integrate and deploy the recommendation engine
- Monitor, fine-tune, and support the model
Step 1. Perform discovery and set goals
First things first, you must define the project goals and set clear performance metrics. To make that happen, check the available data and resources and decide on the expected output. Questions like “Do I really need a recommendation system in the first place?” or “Is there enough data to build one?” should be asked early on.
At Uptech, we can help you answer these and other questions with our software product discovery services. Having an expert team by your side from the very beginning means that you won’t spend money on things you don’t actually need.
Next, together with the ML team, you list the specific tasks for the system. For example, should it drive more sales for a specific shoe model or boost user engagement by encouraging more visits to the website or app? Set these targets early so that later, you can measure how well the system performs in percentage terms. This step also gives an idea of the project timeline and helps choose the best ML approach to build a recommendation system.
The expert team also reviews the quality of the input data – if there is enough information on user behavior, product details, and ratings. If the data is lacking, plan to add extra sources or improve it with data transformation methods. Setting clear goals at this phase gives a roadmap to follow and a way to track progress, making it easier for both the team and you, as the client, to see results as we build the recommendation system.
Step 2. Conduct data collection and preparation
This stage is all about gathering and readying the data for an ML model for a recommendation system. The first task is to check if the data is already available. If not, plan a period to collect enough data based on the expected flow of users. With sufficient data, the ML development team can build a proof-of-concept (PoC) or a minimum viable product (MVP) to move forward.
Next, the team performs an initial analysis of the data to identify key patterns and distributions. For example, they might discover that people aged 25 to 30 often buy a certain product or watch specific films. The ML team also looks for missing data (e.g., some users may visit the site without any interactions) and checks for anomalies – like a sudden spike in power bank purchases during local power outages, which can affect recommendations both in positive and negative ways.
After the initial data analysis is done, ML engineers prepare the features for the model. The process is called feature engineering. There are two types of data used for building a recommendation engine:
- Explicit data includes user ratings, likes/dislikes, reviews, and product comments.
- Implicit data covers user behaviors such as item views, wish list additions, and time spent on content.
Metadata and other attributes can also be incorporated. For users, this might be demographics (age, gender, nationality) and interests, while for items, it might include genre, type, or category.
Finally, the team splits the data into training and testing sets. This step ensures that we can train our model effectively and later evaluate its performance.
Step 3. Choose the approach to create a recommendation system
Based on the methods and features used to predict user preferences, there are 3 main types of recommendation systems:
- content-based filtering,
- collaborative filtering, and
- hybrid filtering.
So, how do recommendation algorithms work? Each of these techniques has its own algorithmic nuances, strengths, and trade-offs. So, let’s take a look at each so you can make an informed decision about which approach fits your case.
Content-based filtering
Content-based filtering (CBF) is an approach where recommendations are based on the characteristics of items. The system analyzes details such as product descriptions, categories, or specific keywords – like genre for media content – and uses this information to form a user profile based on historical preferences. Essentially, the model creates a feature vector for each item – be it a movie, product, or song – and then builds a user profile from the items the user has rated or interacted with (bought/watched, listened to/liked, etc.).
Recommendations are then generated by calculating the similarity between the user's profile and these item feature vectors.
The content-based filtering model example
Let’s take a movie recommendation system as an example. Consider a user who has watched films like Inception and Dunkirk, both directed by Christopher Nolan and known for their intricate plots and long runtimes, featuring standout performances from Cillian Murphy.
A content-based recommender system may analyze attributes such as
- film length
- narrative complexity
- directorial style
- the presence of key actors
Based on this analysis, the system might suggest:
- More Christopher Nolan films, given his signature approach to storytelling and complex narratives.
- Other long-format movies that weave intricate plots similar to Inception and Dunkirk.
- Films featuring Cillian Murphy in roles that echo his performances in Nolan’s works, like Oppenheimer and The Dark Knight.
- Titles with detailed, layered storylines that challenge the viewer's perception.
As the user continues to interact with the platform, the system refines these suggestions, ensuring the recommendations closely align with their evolving preferences.
Content-based filtering pros
- Effective for new users. Content filtering works well for new users; it avoids the cold start problem. For example, on Netflix, when you’re a new user, you indicate the genres you like, and Netflix already has a pre-constructed feature vector of items it can recommend.
- Transparent explanations. This approach relies on explicit attributes – such as genre, rating, and product description – which makes it easy to explain how recommendations are generated.
Content-based filtering cons
- Requires high-quality item descriptions. In this scenario, developers must provide detailed and accurate descriptions of items. Poorly described items may lead to less effective recommendations.
- Limited in capturing implicit preferences. Content filtering might struggle to capture users’ subtle or dynamic tastes. For instance, if a user is in the mood for a comedy one day and a thriller the next, the system may not adjust well to these quick shifts in preference.
Let’s return to our movie recommendation example with Christopher Nolan’s films featuring Cillian Murphy. Just because you enjoy the intricate narratives of Inception and Dunkirk doesn’t mean you’ll only want films that follow the same pattern. It also doesn’t imply that every movie featuring Cillian Murphy will automatically become your favorite.
Considering all the pros and cons, it’s fair to say that content-based filtering works well when there isn’t enough interaction data – as it matches explicit features like director style, film length, and cast, you name it. However, when the system relies solely on these clear-cut characteristics, it tends to ignore the nuanced contributions of other viewers’ tastes.
That’s when collaborative filtering steps in, offering a more rounded approach by tapping into the collective behavior of users.
Collaborative filtering
Collaborative filtering is an approach that generates recommendations based on the behavior of other users. The core idea is that users with similar tastes are likely to enjoy the same items, so users are divided into clusters according to their preferences.
The collaborative filtering example
For example, imagine two users who share similar tastes in running shoes.
User X has tried and liked the Nike Air Zoom Alphafly Next% and the Adidas Adizero Adios Pro, while User Y has enjoyed the Adidas Adizero Adios Pro and the Hoka Carbon X. Since both users like the Adizero Adios Pro, a collaborative filtering system might predict that User X would also enjoy the Hoka Carbon X, and User Y would appreciate the Nike Alphafly.
Of course, this is a simplified illustration. In reality, these systems analyze the behavior of thousands, or even millions, of users to make accurate recommendations.
Memory-based collaborative filtering
This method uses historical rating data that users have assigned to items to calculate the similarity between users or between items.
It is sometimes referred to as neighborhood-based collaborative filtering because it focuses on finding a “neighborhood” of similar users or items. Standard similarity metrics, such as cosine similarity or Euclidean distance, are used in these calculations.
There are two primary techniques within memory-based collaborative filtering:
Item-item collaborative filtering is the technique that makes predictions based on the similarity between items that users have previously rated.
What exactly does that mean? If a user has rated an item, the system recommends other items similar to that one by considering ratings given by all users. A well-known example is Amazon’s “Customers who bought items in your cart also bought” feature, which aggregates similar items to generate real-time recommendations based on a customer’s shopping cart.
User-user collaborative filtering is the approach in which the system compares how different users rate the same items and calculates the similarity of their tastes. Instead of solely recommending items with high ratings, the system groups users with similar interests and offers recommendations based on their historical choices.
This method often results in more personalized suggestions than item-based filtering.
Speaking of the advantages, this approach to building recommendation systems:
- is known for its relative effectiveness and ease of implementation.
- provides intuitive and explainable recommendations.
At the same time, the approach has its limits as it:
- offers limited recommendation variety, often failing to suggest less popular or niche items.
- may struggle with real-time recommendations in environments with sparse interaction data.
- can have scalability issues when handling very large datasets.
Model-based collaborative filtering
When it comes to the model-based collaborative filtering approach, a predictive model is built using historical data to forecast future user preferences. For instance, if you purchase a coffee maker, the system might immediately suggest matching cups.
Models used in this approach include but are not limited to:
- matrix factorization
- clustering techniques
- neural networks (for modeling more complex interactions)
Matrix factorization is a class of collaborative filtering algorithms that decomposes a large user-item interaction matrix into smaller, latent factors. Imagine you have a giant table showing how much each user likes different items.
Matrix factorization “breaks” that table into hidden features – like discovering secret traits of users and items. By learning these latent factors, the system can predict how much a user might like something they haven’t rated yet.
Clustering techniques are a type of unsupervised ML approach that groups similar users or items together. We may think of it as sorting a collection of books into genres without knowing the genre labels in advance – just based on what the books have in common.
Once items or users are clustered, the system can suggest items from the same group, with the assumption that people with similar tastes tend to enjoy similar things. This method is straightforward and helps the system quickly identify trends in user behavior.
Neural networks, especially deep ones, are inspired by how our brains work. They consist of layers of interconnected nodes that process data and learn patterns, even when the relationships are very complex.
In recommender systems, neural networks can capture subtle, nonlinear interactions between users and items. This means that they can pick up on intricate signals in the data that simpler methods might miss, leading to more nuanced and accurate recommendations.
Each of these techniques offers a different way to understand and predict user preferences, and by combining them, we can build recommendation systems that adapt to users’ changing tastes over time.
When it comes to naming the pros, this approach is:
- Better scalability and generalization.
- Capable of capturing complex patterns in the data.
As far as the disadvantages of model-based collaborative filtering it is:
- More challenging to implement and fine-tune.
Hybrid filtering
Hybrid filtering is the approach developed to overcome the shortcomings of using just one recommendation technique. In a hybrid system, the outputs from different models are combined – often with weighted contributions – to achieve more accurate and reliable recommendations. For instance, you might give more weight to collaborative filtering while using content-based filtering to fine-tune the suggestions. In some cases, the results from one approach even serve as extra input for another.
Essentially, nearly all modern recommender systems are hybrid in nature.
Take Spotify’s Discover Weekly as an example. Spotify blends several models to create a playlist that suits your music taste.
It uses:
- collaborative filtering to group users by listening habits
- content-based filtering to analyze song attributes (like artist descriptions and genres)
- audio analysis to extract features directly from the music, such as tempo and chord progressions
Additionally, Spotify employs Natural Language Processing (NLP) to gather public sentiment about songs and artists, which helps further refine the recommendations – especially when dealing with new or lesser-known tracks (the cold start problem).
The combination of these diverse techniques allows hybrid filtering to make the most out of the strengths of each while minimizing their individual limitations.
Step 4. Train and test the ML recommendation model
At this stage, an iterative approach is taken to identify the best model for your needs. It’s better to start by training one model (say, using the collaborative model-based filtering approach) on the prepared dataset and test its performance on unseen data.
Then, the ML team evaluates the model’s accuracy using the performance metrics defined during the discovery phase. Next, you can train another model and compare the results. This trial-and-error method helps select the most effective algorithm and configuration for the specific case.
Hyperparameter tuning is an essential step to achieve optimal performance. ML engineers adjust settings such as the learning rate, regularization strength, or the number of latent factors in matrix factorization. The goal is to enhance accuracy while keeping the model efficient. Each model variant should be tested on the test set using ML metrics like precision, recall, or RMSE to identify the configuration that offers the best balance between performance and speed.
Step 5. Integrate and deploy the recommendation engine
Once a well-performing model has been developed, the next step is integration and deployment. Collaboration with the backend team becomes crucial to wrap the model into an API, which acts as the interface between the recommendation engine and other system components, such as a website or mobile app.
The deployment of the recommendation model happens with containerization tools like Docker, which simplifies dependency management and allows for scalable solutions. Testing in a staging environment is critical to verify that the system responds effectively to real-time requests and can handle increased traffic.
Additional measures, including security protocols and logging systems, are implemented to monitor performance and maintain reliability in production.
Step 6. Monitor, fine-tune, and support the model
After deployment, continuous monitoring and support are key. The model’s performance is tracked in real time by observing key metrics such as user engagement, conversion rates, or prediction accuracy. If a decline in performance or a shift in user behavior (known as concept drift) is detected, new data must be collected promptly, and the model should be retrained or fine-tuned accordingly.
Regular maintenance through periodic reviews and updates keeps the recommendation system relevant and effective over time. This ongoing monitoring and optimization process ensures that the engine remains a valuable tool for driving user satisfaction and supporting business growth.
So, these 6 steps are the roadmap on how to make your own recommendation system using ML.
Challenges When Building a Recommendation Engine
When you build a recommendation system using machine learning, there are a few common challenges to be aware of. But fear not: We’ll look at each one along with possible solutions.
Cold start problem
The cold start problem happens when there is not enough data to make good suggestions. This means the system struggles to recommend items to new users or to promote new products. One way to fix this is to ask users to select a few of their favorite items when they sign up or add detailed descriptions for new products. This helps the engine make better guesses when data is low.
Data sparsity
In many cases, users interact with only a small number of items. This makes it hard to find patterns in the data. A good solution is to use techniques like matrix factorization or add more data from related sources. This extra data helps fill the gaps and improves the quality of recommendations.
Scalability
As the number of users and products grows, the system must work well even with a lot of data. One fix is to use distributed computing or simpler algorithms that run faster. This keeps the engine quick and responsive, even when the load is high.
Model accuracy
Model accuracy is about how well the system predicts what users will like. If the recommendations are off, users will lose interest. Regular updates, trying different models, and adjusting settings can help make the predictions more accurate.
Recommendations diversity
If the engine always suggests the same type of item, users might get bored. It is important to offer a mix of familiar choices and new items. We can add rules that promote variety, so the suggestions include both favorite picks and fresh options.
Each challenge has a simple solution that helps build a better recommendation engine. By paying attention to these points, we can create a system that is both smart and engaging.
How Uptech Can Help Build a Recommendation Engine
Uptech is a software development company that has a proven track record of smart AI and ML solutions that drive real results.
For example, one project reduced medical image processing time by up to 30%, allowing doctors to make faster, more accurate decisions. Another case, Angler AI, has helped brands boost customer acquisition and increase lifetime value through data-driven insights. And with TiredBanker, complex investment reports from the S&P 500 become easy-to-understand insights powered by GPT-4.
These are just a few ML-based projects showing that Uptech has the know-how to create a recommendation engine that fits your business needs. We always focus on understanding your data and goals and design a solution that works with your unique challenges – whether that’s boosting sales, driving user engagement, or improving conversion rates.
Discover how a tailored recommendation system can open new doors for your business. Get in touch today to see how Uptech can support your growth.
FAQs
1. What are some best practices for implementing recommender systems with ML?
Focus on high-quality data, use a variety of methods, and monitor performance closely. Combine techniques and adjust parameters to improve accuracy and diversity.
2. What are the 3 key steps for creating a recommendation system with BigQuery ML?
First, prepare training data in BigQuery. Second, train a recommendation system with BigQuery ML. Third, use the predicted recommendations in production.
3. Which ML algorithm is used for the recommendation system?
Recommendation engines rely on ML algorithms such as clustering models, regression, user-based k-nearest neighbors, matrix factorization, and Bayesian networks to gauge customer preferences.