Types of Recommendation Systems & Their Use Cases

E-commerce and retail companies are utilizing the power of data to boost sales with the help of recommender systems implemented on their websites. The use cases of these systems have been increasing consistently. There could be no better time than now to dive deeper into this excellent machine learning technique.

With the increase in online shopping, the need for giving confidence in buying products has increased. It is why recommender systems are built.

This blog post will look at the types of popular recommender systems, how they work, and their use cases.

Hey there! This blog is almost about 2500+ words long and may take ~10 mins to go through the whole thing. We understand that you might not have that much time.

This is precisely why we made a short video on the topic. It is less than 2 mins, and summarizes how do Recommendation Engines work? & what are the benefits? We hope this helps you learn more and save your time. Cheers!

How does a Recommendation System Work?

Recommendation systems use specialized algorithms and machine learning solutions. Driven by the automated configuration, coordination, and management of machine learning predictive analytics algorithms, the recommendation system can wisely select which filters to apply to a particular user's specific situation. It facilitates marketers to maximize conversions and average order value.

Recommender systems can forecast user ratings, even before they have provided one, making them an effective tool. Mainly, a recommendation system processes data through four phases as follows-

• Collection

Data collected can be explicit (ratings and comments on products) or implicit (page views, order history, etc.).

• Storing

The type of data used to create recommendations can help you decide the kind of storage you should use- NoSQL database, object storage, or standard SQL database.

• Analyzing

The recommender system finds items with similar user engagement data after analysis.

• Filtering

This is the last step where data gets filtered to access the relevant information required to provide recommendations to the user. To enable this, you will need to choose an algorithm suiting the recommendation system.

Types Of Recommendation Systems

Machine learning solves many problems but making product recommendations is a widely known application of machine learning. There are three main types of recommendation systems –

1. Collaborative Filtering

The collaborative filtering method is based on gathering and analyzing data on user’s behavior. This includes the user’s online activities and predicting what they will like based on the similarity with other users.

For example, if user A likes Apple, Banana, and Mango while user B likes Apple, Banana, and Jackfruit, they have similar interests. So, it is highly likely that A would like Jackfruit and B would enjoy Mango. This is how collaborative filtering takes place.

Two kinds of collaborative filtering techniques used are:

• User-User collaborative filtering
• Item-Item collaborative filtering

One of the main advantages of this recommendation system is that it can recommend complex items precisely without understanding the object itself. There is no reliance on machine analyzable content.

2. Content-Based Filtering

Content-based filtering methods are based on the description of a product and a profile of the user’s preferred choices. In this recommendation system, products are described using keywords, and a user profile is built to express the kind of item this user likes.

For instance, if a user likes to watch movies such as Iron Man, the recommender system recommends movies of the superhero genre or films describing Tony Stark.

The central assumption of content-based filtering is that you will also like a similar item if you like a particular item.

3. Hybrid Recommendation Systems

In hybrid recommendation systems, products are recommended using both content-based and collaborative filtering simultaneously to suggest a broader range of products to customers. This recommendation system is up-and-coming and is said to provide more accurate recommendations than other recommender systems.

Netflix is an excellent case in point of a hybrid recommendation system. It makes recommendations by juxtaposing users’ watching and searching habits and finding similar users on that platform. This way, Netflix uses collaborative filtering.

By recommending such shows/movies that share similar traits with those rated highly by the user, Netflix uses content-based filtering. They can also veto the common issues in recommendation systems, such as cold start and data insufficiency issues.

Summing Up

In today’s digital age, including recommendations in systems is an investment worth making. Recommendation systems not only enhance the user experience and engagement but also generate more revenue for businesses.

However, developing a recommendation system takes a significant understanding of data. Your recommendation system is only as effective as it is built to be. At Maruti Techlabs, our machine learning experts can help you solve your business challenges with the help of machine learning services. Our experts are well-versed in deep learning frameworks, supervised learning, unsupervised learning, reinforcement learning, and more.

If you wish to improve your recommendation systems for better business output, get in touch with us today.