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Unpacking Recommender Systems: How They Work and Why They Matter

Digvijay Singh

jan 02, 2023

6 min read

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Introduction

Recommender systems are extremely effective tools for personalizing the experience of your digital product. These are computer programs that recommend items to users based on their preferences, behaviors, and interests. These systems are used in a variety of applications, including online stores (#amazon & #flipkart ), streaming services (#netflix & #spotify), and social media platforms (#facebook & #twitter).

Recommender systems are extremely effective tools for personalizing the experience of your digital product. These are computer programs that recommend items to users based on their preferences, behaviors, and interests. These systems are used in a variety of applications, including online stores (#amazon & #flipkart ), streaming services (#netflix & #spotify), and social media platforms (#facebook & #twitter).

Let's have a look at the different types of approaches used for recommendations:

Collaborative filtering

In a collaborative filtering approach, systems recommend items based on the preferences and ratings of similar users. To understand it better, let's look at it via an example. if two users have both rated/watched a show, the system may recommend a show to one of them if the other watches or rates the show.

The above diagram explains that Robin and Srinivas are two individuals who use a video streaming service. Since both Robin and Srinivas have watched the shows "Breaking Bad" and "Narcos," we can conclude that they have similar interests. As a result, when Robin watches a third show, "Peaky Blinders," our system can recommend the same show to Srinivas.

Content filtering

In a content filtering approach, systems make recommendations based on the characteristics of the items. A content-based recommendation system for streaming services, for example, might suggest shows that are similar to ones that a user has previously listened to, based on features such as genre, type, etc.

In the above diagram, John enjoys watching Breaking Bad and Narcos. We examine the genre and type of these shows as two attributes. Both of these shows fall under the crime and drama genres and types, respectively. Our system can then suggest shows that are of the same or a similar genre, category, or both based on this information. Hence, it suggests "Peaky Blinders."

Hybrid Recommendation Systems

In a hybrid approach, these systems employ both collaborative and content-based filtering techniques. Netflix is a prime example of a hybrid model, employing both collaborative and content filtering techniques to find similar new users with similar traits and content preferences. Using a hybrid model also helps suggest a wider range of products, services, and content to customers by simultaneously applying content and collaborative filtering techniques. In reality, most recommender systems are a complex hybrid of both.

Summary

Following the success stories of platforms like Netflix, Facebook, Spotify, etc. on how they have created a great revenue stream for their businesses by weaving these models into their core offerings, more and more businesses and companies are now considering recommendation systems worth the investment as they are core drivers of high engagement, and improved retention, which results in generating a healthy revenue stream for them. If you found this post interesting, it would mean a lot to me if you could show your support by reacting to this post! It would really make my day—thanks!

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