What is a Recommendation System and What Role Does ML Play in It?

Organizations are encouraged to implement a recommendation system and leverage a machine learning (ML) team to promote their products to their customers. But how do recommendation systems work? What is the difference between a content-based and collaborative filtering-based recommendation system? And how can organizations ensure they achieve ROI here?

Anyone who uses Google, Netflix or any other website or streaming service knows that recommendations are part of using these services. Recommendations are a key tool in marketing today. They are often used as a customer-retention tool and driven by machine learning in marketing.

However, ML is not the only technology at play here. Generative AI development services also help organizations build their recommendation systems at speed.

But what are the key types of recommendation engines? What data is needed to build a machine learning-based recommendation engine? How can recommendation engines improve user experience? What are the common challenges in developing recommendation engines and how do recommendation engines personalize content for users?

This article will answer all these questions, but before that, we’ll start with the basics and discuss how ML processes and recommendation systems work together to deliver results to organizations.

How do recommendation systems work?

ML and recommendation systems work together through algorithms. Or, more specifically, developers use ML to build an algorithm for the recommendation system they’re creating. A machine learning recommendation algorithm utilizes patterns in user data to suggest relevant items to users. These algorithms analyze past behaviors, such as purchase history and browsing patterns, to predict and recommend products that users are likely to find interesting and useful.

This means that ML and recommendation systems work together to collect, store, analyze and filter the data they use to present products they expect customers to like. The process looks like this:

• Step 1 – Gathering the right data. Two types of data are used to build modern recommendation engine algorithms – implicit and explicit data. Implicit data is gathered from user web search history, clicks, cart events, search logs and order histories. Explicit data is collected from customer input, such as reviews and ratings, likes and dislikes, as well as product comments. Gathering these types of data is crucial for any recommendation engine as it helps organizations build customer profiles and identify similarities between products that different customer types may buy.
• Step 2 – Data storage. Once the data is gathered, organizations will need a place to store it. But remember, data pools can get excessively big very quickly. So, to accommodate this, any storage system used to store customer data needs to be scalable to avoid further storage challenges down the line.
• Step 3 – Data analysis. There are three distinct types of analysis organizations use to get the most out of their ML setup and the recommendation system they use. These are real-time, batch and near real-time analysis. Real-time analysis helps organizations process data as it is created, batch analysis helps organizations process data periodically, and near real-time analysis helps organizations slow down processing time from seconds to minutes when they do not need it right away.
• Step 4 – Data filtering. Finally, different matrixes or mathematical rules and formulas are applied to whatever data organizations gather, depending on the type of recommendation they are building or using.

But what are the different types of recommendation system and what pros and cons come with them?

Types of systems

There are three ways organizations can combine the power of ML and their recommendation systems. These are collaborative filtering, content-based filtering and a hybrid of the two.

Collaborative filtering: These recommendation engines collect and analyze data on user behavior, activities and preferences to predict what customers will like, based on the common purchasing patterns they share with other customers. To calculate these, collaborative filtering recommendation engines leverage a matrix-style formula.

Content-based filtering: These recommendation engines use customer profile preferences and item descriptions to work out product similarities by leveraging cosine and Euclidean distances.

Hybrid models: They look at both the meta (collaborative) and transactional (content-based) data. Typically, they outperform both engine types already mentioned in this article. In a hybrid recommendation engine, natural language processing tags can be generated for each product or item and vector equations calculate product similarities. A collaborative filtering matrix is then used to recommend items to users depending on their behaviors, activities and preferences.

Those are the three main types of recommendation system organizations typically use, so now look at their strengths and weaknesses.

Pros and cons

The best thing about collaborative filtering-based recommendation engines is that they don’t require product feature information. This enables organizations to significantly reduce maintenance costs, especially when compared to their content-based counterparts.

However, this engine’s reliance on analyzing customer behavior patterns can create challenges of its own. For example, say a customer needs to move to another country for work and their favorite TV show is unknown in that country. Then the collaborative filtering-based engine running their streaming service will not suggest it to them. This is because no one in that region interacts with that show, and because these types of engines rely on customer interaction to provide recommendations to customers, it is incapable of offering something to them that they might enjoy.

The most prominent advantage of a strong ML and content-based recommendation system is that it doesn’t require customer input to make predictions, which means it can easily overcome the main challenge presented by the collaborative-filtering system. Content-based systems build their recommendations from a customer’s profile and the history that comes with it. Therefore, a well-designed content-based filtering system can identify specific products that will appeal to specific customers at speed, without worrying about whether they appeal to a wider audience.

However, a major con of content-based recommendation engines is they require a lot of upkeep. Attributes must be added and updated constantly to keep recommendations accurate – a daunting task for any organization that sells a lot of products, especially if they sell them across many different industries.

Finally, the biggest advantage of hybrid recommendation systems is that they combine the pros of both content- and collaborative filtering-based models into one system. However, they are much more complex and expensive to build than the other two recommendation system types.

Implementation tips

Three key areas where recommendation engines can quickly achieve ROI for organizations are: providing more cross-selling opportunities, addressing cart abandonment and offering product alternatives to customers whenever possible. Implementing recommendation engines in these three areas makes it easier for organizations to encourage customers to:

Buy items that are not necessarily similar but are related. For example, suggesting paints when customers are frequently buying paintbrushes or other art supplies.

Return to empty shopping carts to complete their purchases.

Browse alternative products other than the top recommendation provided to them, which is especially important as algorithms are fallible and cannot accommodate for things like customers avoiding a recommendation because it is associated with a brand they don’t like.

Implementing recommendation systems with Software Mind

At Software Mind we know that implementing ML and recommendation systems is complex and requires specialized skills.

That is where our experienced software experts come in. They can help choose the recommendation system for you quickly and easily by connecting with you to understand what you need to leverage them for, which in turn will save you significant costs and time. Reach out to us via this form to learn more.