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Recommendation Systems: Understand How Streaming Platforms Work

In recent years, several systems for streaming video, music, and even online games have become more popular. Nowadays, it is very common for people to have subscriptions on one or several of these platforms.

Among the biggest streaming platforms, we can mention Netflix for movies, and Spotify for music. However, over time, and the acceleration that the Covid-19 pandemic caused in the digital world, several other platforms were and are being launched.

3 factors that led to the popularization of streaming systems

All this change that you can see in the way people consume entertainment content is possible for 3 main factors.

First, people’s willingness to choose what they want to watch or listen to, at any time of the day. As a result, the fixed television schedules no longer work well.

Then, easier access to good quality internet, with enough speed to load real-time transmissions. And finally, the availability of more advanced systems and devices also facilitates adherence.

In this sense, the technology involved in streaming systems and their recommendations for users is an important point to consider.

We will cover in more detail below what recommendation systems are and how they work. And that way, you’ll be able to understand why Spotify, for example, can recommend the best songs for you.

How were the recommendation systems developed on streaming platforms?

After the creation of streaming platforms to watch movies and series, listen to music, or even access online games, other systems began to exist as well.

An example is the recommendation systems, which allow the user to choose more assertively what to watch or listen to next.

Even though recommendation systems come with a “customization” promise to the user, they are actually algorithms. They are able to learn which subjects you like the most and start recommending more movies, series, and songs on these preferences.

With this, the user no longer needs to think about the titles they want to watch or tracks they want to listen to. After all, a system is recommending something related to your favorite options.

As a result, people’s experience on streaming platforms ends up being passive rather than active.

How do recommendation systems work?

The recommendation systems used in streaming platforms seek to filter the content available to recommend to users. In order to do this, the systems make use of Deep Learning technology.

There are different methods to make this recommendation system work. Understand each of them below.

Content-Based Filtering

In order to make this method work, recommendations will be made considering the content that the user has already consumed on the platform. For this, the system generates a profile for each user and searches for similar content.

This type of system doesn’t need you to take any actions for good recommendations to happen.

If you listen to pop music on a daily basis, the streaming platform will recommend this type of music so that you can keep listening to what you like the most.

However, there are downsides as well. As in the example above, you will not be introduced to other types of music that might be of interest to you. And the reason? Because you decided that you would listen to pop music using the streaming platform.

Thus, in the long term, the recommendation system starts to fail because the user will always be in contact with the same type of content.

Another disadvantage to be considered is the criterion to determine what is the similarity between contents. Considering movies, for example, what to take into consideration? The actors, the genre, the trailer, among others.

Collaborative Filtering

In this methodology, the system will recommend the content considering the similarity between users. That’s why this is called collaborative filtering.

If you and another user watch the same movie A, when you watch movie B, it will be recommended to the other user as well.

So there is an exchange of recommendations between people who are using the streaming platform and have a similar profile.

You may have already noticed recommendations like “whoever bought this item also bought these other items” in online stores. This type of recommendation is based on the collaborative filtering methodology.

In that case, the problem of having the same type of recommendations all the time ceases to exist.

But it is necessary to consider a point of attention. How do you know if the user really liked the content they watched, listened to, or read? In that sense, it’s interesting that users themselves evaluate their experiences with the content in order to support the recommendation system in the future.

Other points that can help the Deep Learning system is the time that the user kept watching the content, or how many times this content was accessed.

Hybrid Systems

This recommendation method makes it possible to put together the characteristics of Content-Based Filtering and Collaborative Filtering systems.

As a result of the union, the tendency is for the recommendations to be more assertive in this system than in previous individual ones.

In practice, the recommendation system will have separate recommendations, and by using an algorithm, they will be combined into a single list.

Cold Start

You might be thinking: but what about new users? This is where the Cold-Start recommendation system presents itself.

The challenge is big because there is no collection of information about these new users. And in some cases, when there is no need to log in to consume content, new users can exist for a long time.

To make Cold-Start work, lists are created with general recommendations. On Spotify, for example, you can find the list of the most played songs in Brazil or in the world. There are also lists that use the most accessed content in the last week to make this type of recommendation.

In addition, if it is possible to obtain the user’s location information, it is possible to indicate the most accessed contents in a certain region.

Issues related to streaming platform recommendations

As you have noticed, recommendation systems need a lot of technology and a lot of data. Every action users perform on a streaming platform generates data, which the systems analyze and result in the recommendations we receive on a daily basis.

However, when the platform uses a method like Content-Based Filtering, there is a risk that you will end up stuck with just one type of content.

Many people don’t pay attention or bother about that, after all, they are getting recommendations for things they like. However, it is interesting to always be aware of this type of recommendation. To protect yourself, try to get out of your comfort zone and get in touch with content on different subjects.

Whether these contents are music, movies, series, news, books, or e-commerce items. This variation will be beneficial and will show you plenty of other options.

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