How Do Recommendation Engines Work?

How Do Recommendation Engines Work

Big tech companies know exactly what’s on your mind…It’s not magic but technology.

Picture this: You just finished a film on Netflix and want to follow it up with something similar. Luckily, Netflix comes to the rescue and gives you the perfect suggestions to continue your weekend movie binge. This isn’t just a hypothetical scenario but something a lot of people actually go through. 

Over 80% of all TV shows and films people watch on Netflix are based on the platform’s recommendation system. But Netflix isn’t the only platform that suggests content you may like. Others, like Spotify, YouTube, and even dating platforms like Tinder and Bumble, have recommendation algorithms in place to predict what suits you best. If you are curious about how platforms typically make recommendations, look no further! Here’s how recommendation engines work and an overview of the Netflix recommendation system in particular. 

What is a recommendation engine?

Be it Amazon, eBay or those platforms we mentioned above, almost every website you use today has some iteration of a recommendation system. Recommendation engines use machine learning algorithms to filter data on a platform and find the products that would be the most relevant to a particular user. These recommendation engines allow businesses to provide customers with a personalized experience and help maintain a competitive edge. 

There are three main types of recommendation systems in use today—collaborative filtering, content-based filtering and hybrid recommendation systems. 

Collaborative filtering

Collaborative filtering takes a user’s behavioral information and compares it to that of others to determine what they would like. Suppose person X likes horror, thriller and sci-fi films, and person Y likes sci-fi, crime and horror. The collaborative filtering system would assume the two had similar interests and would thus recommend crime to person X and thriller to person Y. 

The advantage of collaborative filtering, particularly in recommending films and TV shows, is that they can make suggestions without having any understanding of the specific content of a film or TV show. However, the drawback of this approach is that it assumes that people who like the same things in the past will also continue to have similar preferences in the future.

Content-based filtering

Content-based filtering relies on the keywords defining an item to recommend items to users based on what they previously liked. For instance, if you liked In The Mood for Love (starring Tony Leung and Maggie Cheung) by Hong Kong-based film director Wong Kar Wai, your streaming platform’s recommendation algorithm would suggest either more films by the same director or others featuring Leung and Cheung. 

While this seems like a surefire way to provide users with content that pertains to their tastes, it does have a major downside. This system has limited knowledge of the user and can only make suggestions based on what you have already consumed but not anything beyond that. For instance, if you use a platform with content-based recommendations to buy coffee, it would only suggest other kinds of coffee products to you and nothing else. 

Hybrid recommendation systems 

Finally, we have a combination of both collaborative and content-based systems in the hybrid recommendation system. This system uses natural language processing to create tags for every item on the platform and then compares the similarity between different items. This system has been found to give more accurate results since it overcomes the shortcomings of the previous two systems. 

Understanding Netflix’s recommendation system

Going back to the Netflix example. The streaming platform uses the hybrid approach as well as its own specific nuanced systems to give more precise recommendations. Netflix calls its recommendation engine a “three-legged stool”:

  • the first leg of this stool is based on viewer data, such as what they are watching and what they consume before and afterward;
  • the second leg is the detailed list of tags generated by Netflix’s staff to categorize each show; and
  • the final leg is their machine learning algorithm that takes viewership patterns into account, such as a user’s time spent watching a show and how much importance is given to what they view and how they view it. 

If you want the best recommendations from the Netflix algorithm, the first thing you need to do is rate the content you watch. Its recommendation system combines your rating with that of other viewers with similar tastes and uses them as a basis for future suggestions. The system uses this data to give you percentage data on how likely it is to match your tastes. Another thing you can do is add films and TV shows to your list. Not only can you then keep tabs on what you need to watch next, but it will also help the platform learn your preferences to suggest similar content.

Overall, recommendation engines are a powerful tool not just for business owners but also for users to find what they are looking for with greater ease. These systems are so effective that it is expected that the recommendation engine market is expected to reach US$54 billion by 2030. For business owners out there who want to grow their customer base, enhancing customer experience strategies and learning how recommendation systems work should be one of your first priorities. 

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Header image courtesy of Freepik


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