What is a recommendation engine?

Recommendation engines help users discover content, products or services they might not have found on their own. These systems are an integral part of generating sales and cultivating engagement for many online businesses, including e-commerce websites, media streaming platforms, search engines and social media networks.

A recommender suggests the next movie or video to watch, a similar song to listen to, relevant search results or a product that complements a certain order.

The suggestions created by recommendation systems also play a vital role in personalizing user experiences. According to research by management consulting firm McKinsey , personalization can raise revenues by 5%–15%. Also, 76% of customers feel frustrated when they do not experience personalized interactions.

The market for recommendation systems is growing. In 2024, the recommendation engine market  is estimated at USD 6.88 billion, and this market size is projected to triple in 5 years.

To target users with suitable suggestions, a recommendation engine blends data science and machine learning.

Recommenders typically operate in 5 phases to predict the most accurate recommendations:

Recommenders differ based on the filtering method that they use. There are generally 3 types of recommendation engines:

A collaborative filtering system filters suggestions based on a particular user’s likeness to others. Collaborative recommender systems rely on explicit and implicit data and assume that users with comparable preferences will likely be interested in the same items and potentially interact with them in similar ways in the future.

Amazon , for instance, uses collaborative filtering for its product recommendations, just like Spotify does for its audio content.

Collaborative filtering recommenders can offer effective suggestions and won’t usually need detailed item descriptions. However, collaborative filtering is also prone to the cold start problem, which happens when the system has limited historical data to draw from, especially for new users.

There are 2 main kinds of collaborative filtering systems: memory-based and model-based.

Memory-based systems represent users and items as a matrix. They are an extension of the k-nearest neighbors (KNN) algorithm because they aim to find their “nearest neighbors,” which can be similar users or similar items. Memory-based systems are further subdivided into 2 types:

• User-based filtering computes similarities between a particular user and all other users in the matrix. User-based similarity functions are calculated between rows in the user-item matrix.

• Item-based filtering computes item similarity through user behavior (how users interact with items and not item features). Item-based similarity functions are calculated between columns in the user-item matrix.

Alternatively, model-based systems create a predictive machine learning model of the data. The user-item matrix serves as the training data set for the model, which then yields predictions for missing values, that is, items that a user has not yet found and will therefore be recommended.

One of the most commonly used model-based collaborative filtering algorithms is matrix factorization. This dimensionality reduction method decomposes the often large user-item matrix into two smaller matrices—one for users and another for items—having a select few dimensions. The 2 matrices are then multiplied together to predict the missing values (or the recommendations) in the larger matrix.

A more advanced implementation of matrix factorization harnesses deep learning neural networks. Other model-based systems employ machine learning algorithms such as Bayes classifiers, clustering and decision trees.

A content-based filtering system filters recommendations based on an item’s features. Content-based recommender systems assume that if a user likes a particular item, they will also like another similar item. Content-based filtering considers item descriptions such as color, category, price and other metadata assigned by keywords and tags, along with explicit and implicit data.

Content-based filtering systems represent items and users as vectors in a vector space. Proximity is used to determine the similarity between items. The closer 2 vectors are in space, the more similar they’re considered to be. Vectors similar to previous items according to their supplied features will be recommended to the user.

Content-based recommenders apply a user-based classifier or regression model. Descriptions and features of items a user is interested in act as the model’s training data set, which then yields predictions for recommended items.

Content-based recommendation systems can be further improved by using natural language processing tags. However, this tagging process can be tedious for huge volumes of data.

Unlike collaborative filtering, the cold start problem is less of an issue since content-based filtering is based on metadata characteristics rather than past user interaction. However, content-based filtering can be limited in exploring new items, as it often suggests those similar to what users liked previously.

As its name implies, a hybrid recommendation system merges collaborative filtering and content-based filtering.

As a result, a hybrid approach can greatly enhance the performance of a recommendation engine, but it requires advanced architectures and intensive computational power.

Netflix, for instance, uses a hybrid recommendation system for its movie and TV show recommendations.

A recommendation engine can bring value to both businesses and users. Here are some advantages organizations can gain from investing in recommendation systems:

Recommending the right product or service saves users time from scrolling endlessly through an extensive catalog. For instance, 80% of what viewers watch on Netflix comes from suggestions powered by recommendation algorithms. Moreover, suggesting relevant content leads to personalized experiences.

According to research from McKinsey, an enhanced customer experience translates to higher customer satisfaction, 20% higher, to be exact. Satisfied customers become more engaged and develop loyalty toward a brand, enabling enterprises to build trust and retain more customers.

Delivering personalized recommendations encourages users to view and click through more items, which might eventually convert perusers into purchasers. McKinsey found a 10%–15% rise in sales-conversion rates from more positive and personalized customer experiences.

Customer conversions drive sales, and sales drive revenue. McKinsey notes that 35% of what shoppers buy on Amazon comes from product recommendations. Meanwhile, Netflix estimates savings of more than USD 1 billion due to its recommender system.

Recommendation systems have their limitations and pose challenges for organizations. Here are a few of the most common ones:

Recommendation engines entail analyzing and filtering massive amounts of data. This requires complex architectures and a significant investment in computing resources.

Recommender systems need to be fast enough to determine and display the right recommendations in real time. This becomes even harder when real-time suggestions are served simultaneously to hundreds or thousands of users, let alone millions of them.

Optimizing machine learning algorithms around the wrong metrics can lead to irrelevant recommendations. Items that are often highly rated might be suggested more frequently than new or obscure ones or those with fewer reviews. However, the most popular or most purchased items might not be what customers are interested in.

Machine learning algorithms might learn societal biases present in data—be it the collected data, the tagged data, the training data or external data sources—or from human evaluators who tune the model. This can result in inaccurate recommendations.

Some users might opt out of an enterprise’s data-gathering initiatives for recommendation systems due to privacy concerns. Businesses might also need to consider regulatory requirements and compliance standards when collecting and storing data.

Here are some ways enterprises can use recommendation systems. Other use cases and applications might crop up as recommender technology evolves.