Importance of Recommendation Systems with the example of Netflix

Everything is a Recommendation

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CodeX

5 min readOct 7, 2021

In this article, we will gain awareness about the importance of recommendation systems. We will quickly start with Netflix story followed by the incredible recommendation system that goes on in the background. At the end of the article, I will show you how all of this works with Python.

I will not go into the details of the recommender systems theoretically in order not to extend it too much. I want to talk about this topic in the following articles.

Product personalization is awareness.

All this starts with making sense of the big data held and using it for the right purpose. Services need to want customers to be aware of how companies are adapting to their tastes. This not only promotes trust in the system but encourages members to give feedback that will result in better recommendations.

Recommender systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors. We are talking about a concept that can be integrated into every sector you can think of, such as banking, e-commerce and the retail sector.

Let’s start with Netflix story!

I think all we know that Netflix was purely a movie rental service in 1997. Users ordered movies on the Netflix website and received DVDs in the post. When they were finished with them, they would simply post them back to Netflix in the envelopes provided. Could you imagine at those days how seen as a boon this service?

Today, Netflix streams movies and has more than 209 million paid subscribers in over 190 countries around the world. It offers a wide range of TV series, documentaries, and feature films across a wide variety of genres and languages, including original productions.

In 1998, Netflix launches the first DVD rental and sales site, Netflix.com.

We can easily say that the exciting part starts from here.

In 2000, A personalized movie recommendation system is introduced, using members’ ratings on past titles to accurately predict future choices.

The company also launches its “Netflix Prize” a machine learning and data mining competition for movie rating prediction in 2006. Netflix sponsored a competition, offering a grand prize of $1,000,000 to the team that could take an offered dataset of over 100 million movie ratings and return recommendations that were 10% more accurate than those offered by the company’s existing recommender system.

To explore this technical process more closely, you can read Simon Funk’s blog post, he shared his findings with the research community

100 million anonymous movie ratings, using a system that rates films from one to five stars. This was the largest set of such data released to date.

Netflix’s focus on recommendation algorithms has changed is because Netflix as a whole has changed dramatically in the last few years. Netflix launched an instant streaming service in 2007, one year after the Netflix Prize began.

Do you know this too ? Movie images change according to your interests.

Let us consider trying to personalize the image Netflix use to depict the movie Good Will Hunting. Here system might personalize this decision based on how much a member prefers different genres and themes. Someone who has watched many romantic movies may be interested in Good Will Hunting if Netflix shows the artwork containing Matt Damon and Minnie Driver, whereas, a member who has watched many comedies might be drawn to the movie if Netflix uses the artwork containing Robin Williams, a well-known comedian.

Now let’s get to know these movie recommendation methods a little more closely

Recommender systems can be classified into 2 types:

I will not go deep into these all methods. In my opinion, those recommender methods are very powerful and open to implement many domains. There will be another articles that may have more technical details.

Collaborative filtering methods have 3 different sub methods like user-based collaborative, item-based collaborative and model based collaborative (Matrix Factorization) filtering.

Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. This family of methods became widely known during the Netflix prize challenge due to its effectiveness as reported by Simon Funk in his 2006 blog post.

But today, I will clearly show you the user-based method, which is one of the collaborative filtering methods.

The idea of collaborative filtering is simple: User group behavior is used to make recommendations to other users. Since the recommendation is based on the preferences of other users it is called collaborative.