Netflix Algorithm & Global Recommendation Settings

Recommender Settings and You

Big data and algorithms have made a substantial impact on modern society. Many companies are basing most of their moves on the identification of real-time information, like production companies who use big data to cut production costs. Production companies are not the only companies benefiting from these new technologies because entertainment is also changing. More and more people are expecting personalized services or recommendations, which includes entertainment. This is why Netflix has become such an influential entertainment giant. Netflix has been in the forefront of how their entertainment titles are presented to their customers.

In the infographic below, created by the New Jersey Institute of Technology’s Online Masters in Computer Science program, we take a closer look at recommender settings and their influence on what the world watches.

Recommendation Settings

The People’s Preference

Experts have seen a trend in the way people interact with services, products, and businesses. The trend shows that people prefer services or products that cater to their needs or desires. People are willing to share a little personal information so that they are shown offers they might like. Netflix is one of the first entertainment companies to pick up on this trend, and they have made advancements in their algorithms to make better recommendations.

Netflix understands that their clients do not want to browse through thousands of titles that hold no interest to them. People want choices, but they want choices that they are interested in.

It should be noted that this streaming giant has timed how long they have to keep customers interested in the content they are shown. It seems that the entertainment giant only has 90 seconds to convince a customer that a film or show is something they might enjoy.

Most people are impatient, especially if what is being offered holds no interest to them. Think of a child who is being shown candy choices with flavors that do not seem appetizing compared to a child being shown flavors of all the fruits he or she has enjoyed in the past.

How Important Is Preference to Netflix?

This streaming giant now has 75 million subscribers all around the world, and they can thank their recommendation system for that. Many customers who talk about this company speak on how the service has exposed them to new shows that they love.

Netflix’s guessing program has improved over the years and with reason since about 75 percent of all activity on the site is shaped by the website’s recommendations. People who sign up for this streaming service are interested in watching shows or movies they have seen, but they are mostly interested in seeing something new.

The streaming company cannot afford to be wrong with their recommendations too often because subscribers will start to think that the site does not have good content to offer. This is something that the streaming giant knows, which is why they are so invested in improving their recommendation system.

One thing that Netflix does is reset the algorithm on each person every 24 hours to make sure their recommendation list is updated with new activity.

There are two specific algorithms used at the moment for those interested in a little more detail on how the streaming site has accomplished their sophisticated recommendation system. The first one is called a Restricted Boltzman Machines or RBM. The RBM is a program that learns probability the more input it receives. This is one reason why Netflix wants people to run through some titles and share if they like or dislike the title. The RBM is more effective if the user judges many titles.

The second algorithm is the Matrix Factorization, which is an algorithm that helps the streaming service predict how much a person might like a film or show. The program uses a complicated formula that tries to discover matrices that, when multiplied, will end with the original matrix. The matrix and matrices are gathered through user ratings, which is another reason why the streaming service urges users to give ratings to the shows or films that they have watched.

There is no doubt that Netflix takes their client’s preferences very seriously because it is obvious that the entire streaming service depends on their ability to make good recommendations.

Challenges are Always on the Horizon

There will always be challengers no matter how much the streaming company has moved forward. Still, challenges usually drive positive changes and help with the advancement of future technologies.

Netflix and other similar companies like Spotify or Pandora have been struggling with a few hiccups in the way they predict what a user might enjoy. One of the issues that the streaming company is facing is it does account for language too well. There are 21 languages available on Netflix, but they only show popular titles of that language. The algorithm simply does not factor in personalized customer options that he or she will enjoy in his or her native language.

Context is another hurdle that has not been successfully dealt with. There are many reasons why a person might like a title, which are easily detectable through their working systems. This includes something as simple as genre, and genre types, or actors involved with the project. What is impossible to really pinpoint is the kind of context that someone usually leans to. Predicting what someone might like was just the first step; it seems that Netflix now wants to learn why their customers like a particular title.

Another issue that the Internet company wants to solve is the cultural differences between viewers. In short, the company wants to learn to predict how certain cultures might perceive the theme or the context of a particular show or film before it is recommended.

There is no doubt that Netflix continues to seek new ways to personalize the experience users have on their site, which should attract even more customers and make them loyal. Society is definitely changing, and the way this company works with their customers might be a good example of what people are now expecting from the businesses they subscribe to. Indeed, society has never been so people-driven, and big data, along with algorithms, is making it easier to give consumers what they crave.

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