Building Effective Customer Recommendation Systems: Data, Algorithms, and Avoiding Errors

Did you know that personalized recommendations account for 35% of Amazon's revenue? Well, now you do! Why is this important??

Every customer wants a personalized experience, sometimes they even want you to preempt their needs even before it is fully formed in their own mind. This ability to anticipate customer preferences is a key reason Amazon remains the world's top e-commerce platform. By leveraging data from past purchases and trends among similar demographics, Amazon delivers tailored suggestions that keep customers engaged and coming back.?

Now, as AI and machine learning continues to improve everyday, there is the hope that the process for building a recommender system will become cheaper, however it is always a good idea to understand the inner workings of how this system can be built and how it can improve your business and even if you need it at all.

We’ll explore what it takes to build an effective recommendation system, including the types of data required, algorithms, and other key considerations for success.

Do you even need a recommender system?

When you are at the nexus of business and technology, this is an important question to ask. You do not want to be the manager that brings up the great idea but will not get the buy-in from internal stakeholders all because you cannot effectively present:

• Why a recommender system is important for the business at this stage.
• The cost-benefit analysis of building a recommender system.?
• How it has benefited competitors within the same industry.
• How will we build it taking into account factors specific to the business

Most of the use-cases you will find out there surround media streaming and e-commerce? platforms such as Netflix and Amazon. However, there is growing interest in them in banking and finance, health especially as individual customers within these industries continue to clamor for personalized services as no two individual investment tastes or bodies are the same. So, there may be an opportunity to use a recommender system to improve your business, you just need to look hard enough and be honest with your answers especially if you are in the B2C industry.??

Data is important but the ‘type of Data’ utilized is more important!

So cheers! you have answered the initial hard questions and have gotten buy-in from the internal stakeholder. The good news is you have the green-light to move ahead with the product but the not-so-good news is, that was not the hard part. The first step into ensuring you are building an effective recommender system is to decide on the data you want to use - collaborative filtering, content based filtering or a hybrid approach.?

• Collaborative filtering utilizes the data of customers with similar interests to make recommendations to another customer. For instance, if male subscribers between the ages of 20-40 on Netflix enjoy watching action, thrillers, and comedies, a customer within this demographic will have similar suggestions upon logging into the platform. These suggestions can work in two ways: User-to-user filtering — Recommends movies that users with similar viewing habits have enjoyed. For example, if User A and User B both like action movies, Netflix may recommend a thriller that User B has already watched to User A; Item-to-item filtering — Recommends movies similar to what the customer has previously watched. For example, if a customer watches a specific action movie, Netflix may recommend other titles within the same genre or with similar themes. Therefore, collaborative filtering relies on historical data of individuals with similar interests or choices to predict recommendations.

• Content-based filtering, while similar to item-to-item collaborative filtering, differs in that it focuses solely on the individual customer's preferences as derived from their historical data. Instead of relying on similarities between users or the behavior of others, it analyzes the specific attributes of items the customer has previously selected. For instance, using Netflix as an example, if a customer enjoys action movies, content-based filtering will recommend other titles from the action genre based on features like plot, cast, or theme. However, it will not branch out to genres like thrillers or dramas, even if they are somewhat similar. This contrasts with item-to-item collaborative filtering, where such similarities might lead to broader recommendations.

• The Hybrid Approach combines both collaborative filtering and content-based filtering, leveraging the strengths of both methods to enhance recommendation accuracy. As you might guess, this approach can be expensive to develop and maintain, and it may not always be necessary unless your platform has a large customer base or diverse user preferences. It’s important to evaluate the scale and complexity of your audience before deciding if a hybrid approach is the right choice.

In choosing the right approach for your business, there are several factors to look at, even beyond the size of the customer base, like what service or product do you provide, cost and I don't just mean the building costs but also the maintenance cost because as you will see below, you just don’t build it and leave it.?

Models, Algorithms, and Into the Deep Learning

Now that you’ve got a sense of what kind of data you should be looking to gather and process, you should know that there are various deep learning models or techniques that can be utilized to automate the recommendation process. For instance, Neural Collaborative Filtering (NCF) builds on traditional collaborative filtering by integrating neural networks. Unlike basic collaborative methods that simply recommend items based on similar users or items, NCF can uncover more complex, non-linear patterns in user behavior. For example, it might recognize that a customer who buys specific types of books also has an interest in related genres or products, even if they've never explicitly shown interest in those items. By using deep learning models, NCF creates a personalized experience that goes beyond simple item-to-item recommendations. It can also predict preferences for items the user hasn't interacted with yet, making it a powerful tool for recommending content or products a customer may not have initially considered.

On the other hand, Reinforcement Learning (RL) is a technique that adapts to real-time user interactions. Unlike other models that rely on historical data, RL continuously learns from feedback received from users. For example, imagine a video streaming platform like YouTube, where the system learns from each click, pause, or skip to improve its next suggestion. The model doesn't just learn from what the user likes but also from how they engage with content over time. By optimizing recommendations based on this continuous feedback loop, RL systems can predict what users will enjoy next with increasing accuracy. This makes RL an ideal choice for platforms that need to keep their recommendations dynamic and responsive, ensuring users are always offered fresh, relevant content.

There are various other machine learning algorithms that can be leveraged and you can even mix and match if you want! But the best choice depends on your specific business needs. This is why it's important to consult with experts like Intelia to help determine the most effective solution for your goals. Visit Intelia for more information.

Make Sure to Side-Step Errors

One common pitfall when building a recommendation system is forgetting the foundational principles of working with big data, you must be cautious of errors! Issues like overfitting and underfitting can compromise the system's effectiveness and user experience.

Overfitting (caused by high variance) occurs when your system becomes too tailored to the training data, capturing noise instead of meaningful patterns. For example, if a customer once watched an obscure indie movie, and the system exclusively recommends similar niche films, it risks overlooking their broader preferences, such as action or comedy. This makes the recommendations overly specific and irrelevant to the user's overall taste. To avoid this, careful attention must be paid during the data selection process and feature engineering—the data used to generate recommendations must be diverse and representative of the user base.

Underfitting (caused by high bias), on the other hand, happens when the system is too simplistic and fails to identify critical trends. For instance, if a user enjoys a mix of action, thrillers, and sci-fi, but the system only recognizes "popular movies" as a category, it might recommend generic blockbusters, ignoring the user’s nuanced preferences. This lack of sophistication can leave customers feeling underserved and disengaged.

To deliver accurate and engaging recommendations, your system needs to strike a balance, flexible enough to uncover meaningful patterns in diverse data while avoiding errors that reduce its effectiveness.?

Keep Training and Testing your Model?

As I mentioned earlier, building a recommendation system is not a one-time project. Companies like Amazon and Netflix invest millions of dollars into continuously improving their recommendation systems. Why? Because doing so not only boosts their revenue but also ensures they remain top-of-mind for customers, keeping them engaged and loyal to the platform.

When developing your own recommendation system, it’s crucial to prioritize ongoing maintenance through regular training and testing. A best practice is to split your data, using 80% for training the model and reserving 20% for testing. This approach helps ensure the system delivers accurate and consistent results with unknown variables while minimizing errors such as bias or variance, which can significantly impact the quality of your recommendations.

Another essential step in ensuring the quality of your recommendation system is to conduct A/B testing to evaluate the impact of its recommendations on customer behavior. Honesty in this process is crucial; even if the results are neutral rather than positive, it’s important to avoid influencing outcomes to maintain integrity.

Given the rigorous and often costly nature of building and maintaining a recommendation system, it’s advisable to test your ideas on a smaller scale before committing to a full-scale implementation.?

At Intelia, our team of experts is ready to guide you through the entire process, whether you’re looking to test the waters or fully dive into building a robust recommendation system.