case study on netflix recommendation system

Netflix Recommendation Engine - How Netflix uses Big data and Analytics to Recommend you your Favourite Shows

Harshit Verma

Entertainment is probably as old as the era of humans itself. We have found out different ways of getting entertained. Some of the sources include dance, singing, playing but some of the most famous and widely accepted ways of entertainment are films, theatrics and movies.

In this 21st century, as the internet penetrates every domain, it has not left the entertainment sector per se. It has boosted the domain to such heights that it is probably hard to go back to square one. The topmost entertainment provider in the world is Netflix. It uses technology to scale great heights and great revenues.

There was an old film with dialogue where the protagonist says “A film works only when it has three elements to it, Entertainment, Entertainment and Entertainment”. Well, we as viewers might be tempted to say yes it is true but is it still the same in the twenty-first century? The answer may be a little more than just entertainment. It might include promotions, marketing and more. What more you ask? Big data, Artificial intelligence, machine learning.

Netflix, the prime entertainment host, do it all to cater to your entertainment needs. We will dive deep to understand how Netflix uses its recommendation engine and how it has incorporated this super-tech to reach new heights.

A little preface about Netflix Personalised Entertainment/Content on Netflix Data Analytics of Netflix The Recommendation Engine of Netflix Business Verticals of Netflix Some Facts about Netflix that might Interest you FAQ

A little Preface about Netflix

Netflix is a streaming service that offers a wide variety of movies, TV series, shows, anime, documentaries, and more. As mentioned, it is a streaming service, so it can be accessed on every possible device. You can stream Netflix via the official website, or its android or IOS app.

You can tune into it instantly on the web at netflix.com from your personal computer or on any internet-connected device that offers the Netflix app, including smart TVs, smartphones, tablets, streaming media players and game consoles . It is a monthly subscribed service, which you have to redeem monthly.

There is always something to watch on Netflix. So much so that it has a full library of entertainment. It is extensively built for the best experience in entertainment to its subscribers. That is why Netflix is the most famous streaming platform in the world.

You might wonder that entertainment is top-notch on Netflix but there is one more thing that it pays huge attention to. The thing is not hideous but is often not much talked about. That one aspect is the library and the whole management of this extensively built personalised library of content.

Netflix, for years, is able to provide personalised content recommendations to its each and every subscriber. How does it do that? What is the magic behind it? let us uncover that.

Personalised Entertainment/Content on Netflix

“If the Starbucks secret is a smile when you get your latte… ours is that the Web site adapts to the individual’s taste.” - Reed Hastings (CEO of Netflix)

Over the course of the last few years, Netflix has become the favourite destination of people who want to binge on some entertainment films and shows. Netflix started as a humble DVD rental business and it later turned into something totally different as technology kicked in.

We can see the huge subscriber base of Netflix as proof of work and growth. One of the most crucial elements of this growth is personalised content. That crucial element is the underlying asset of the presence of Big Data and artificial intelligence.

Netflix doesn't just work in managing content, movies, TV shows and entertainment but it has a lot of other data to handle as well. It has user insights, their data and usage patterns and everything connected to them and of course ‘us’.

The data management part is not easy at all, especially when you have to constantly change to adapt to your surroundings. Netflix does it so well, no wonder it uses Big data to manage and make sense of huge piles of useful data.

“Where there is data smoke, there is business fire.” — Thomas Redman

If we see the graph of Netflix's memberships and subscriptions, we can see a beautiful upward direction to the moon. The reason is its personalised services and the best user interface that is available out in the whole world.

The revenue of this streaming giant is also similar to that of its subscriber base. It has grown steeply and steadily. The reason is the efficiency undoubtedly.

When it first started as a DVD rental service, it was a quite simple video provider. It used to use mails to provide DVD copies of the content. It was in 2010 when Netflix thought of rebranding and using more sophisticated technology as an aid. They began streaming online and the data that they were collecting grew many folds. This marks many years of anniversary for Netflix as a data-driven company. It has been data-driven even from its very inception.

Their “Data Analytics' team work very closely with decision-makers of the company. The data team has useful insights, metrics, predictions and analytics so that everyone can work efficiently. They have to work super closely with the product teams, content teams, studio and marketing teams and altogether with the business operations.

With the data they collect, they have to perform context-rich analysis to provide insight into their business, partners and of course their subscribers or members. This also enriches the experience for Netflix.

Data Analytics of Netflix

When you are dealing with huge amounts of data then efficient data management becomes the reason and a necessary condition for your success. At Netflix, data analytics is the backbone of every work that they do. It is the metric at which they measure their location. It is the basis to know where they are and essentially where they are going. This is where Netflix finds and experiments, it is also the place where they solve existing problems.

Even from the DVD days, they are a data-driven company first and then anything else. From its inception, they have grown their data department to new heights every now and then.

As Netflix grew, the need to manage data effectively and efficiently grew too. Every decision is fueled by the data behind it. If you are into any business in the world, you need data to do your best possible job. Netflix does it and it does it quite efficiently.

Data Science and Engineering at Netflix is primarily and supremely is directed at improving various aspects of the streaming business. Among all the other roles, research applications span many areas including Netflix's personalization algorithms, content valuation, and optimization for future streaming.

To maximise the already big impact of Netflix’s research, they do not centralise research into a separate organisation. Instead, they do it within altogether other departments. They have many teams that pursue research in collaboration with business teams , engineering teams, and other researchers. This enables closer partnerships between researchers and the business and engineering in each and every area.

In addition to that, research that applies to the same methodological area or business area is shared and highlighted in discussion and debate forums to strengthen the work and its impact.

When we think about big data and Netflix, what comes to mind? More than often you would think that it has something to do with the content recommendation algorithm or the streaming to your personal device. Yes, you are right in most senses, these two topics are the main contributors for data research and analytics but there is more.

They both are an integral part but there is more to the whole picture. So, further data is used to “make the experience even better than before”. Data has to do a lot with questions like “Which piece of content makes our customers or members most joyous” or “What are some of the areas in which Netflix can collaborate to provide 360-degree entertainment”.

Data solves the problem of finding the right market fit for the product in any sort of market. Which in turn enhances the user experience of Netflix as a whole.

The Recommendation Engine of Netflix

As we discussed previously, data is fuel for Netflix's smoothness and convenience. The motive is to constantly improve the predictions on how someone is going to react after watching a certain type of movie, genre and another basis. This helps in knowing about the customer preferences, which can be used in future for making better predictions.

This is when their recommendation algorithm comes into the picture. Netflix has, over the years, designed an algorithm that can suggest recommendations to its users. It is called the Netflix recommendation Engine or NRE. it has been reported that 80% of Netflix viewer activity is driven by personalised recommendations from the engine. Which is a pretty good number for a streaming platform like Netflix. It also saves marketing costs for the streaming giant.

In Netflix’s case, the NRE or the Netflix recommendation engine has some different factors of inputs. It collects data that will be the most relevant in the prediction of user behaviours. Some of the most commonly tracked inputs are as follows,

• The device used to stream on.
• The number of searches.
• If the show was paused or fast-forwarded.
• Whether the entire show/movie was completed watching.
• Whether the viewer gave the show or movie a thumbs up or thumbs down.
• Scenes that the user replayed.
• Time and date at user watched a show/movie.
• Profile information such as age, gender, location.

These are some most used inputs that Netflix recommendation engines use. Moreover, of all the websites that use big data and other predicting technologies, Netflix does it the smoothest. It has been reported that 47% of North Americans prefer to use Netflix with an exclamatory 93 % retention basis . This marks proof of the efficient working of the Netflix model.

Nevertheless, Netflix is not just winning because of its near-perfect prediction and recommendation technology but also good management. Let us know a little about the business verticals at the heart of this streaming giant.

Business Verticals of Netflix

What you see is the content and recommendations, well stacked on Netflix, what you do not see is the work that goes behind curtains. There are business verticals/segments that work as a team to improve how we binge-watch content online. Let us read about them in brief words.

Product is the actual product that the streaming giant is providing. It is the segment that deals with the Netflix app. The motive of this department or business segment is to deliver high-quality streaming, smooth user interface , best customer service. The product segment also has to ensure that the members get the right content recommendations at the right time.

The content segment is the cream for the cake. At the heart of Netflix, it also is a content producing company. The content vertical is accountable and responsible for licensing and enabling shows and movies for Netflix. This department also works on all things that can be joyous to the public. Buying decisions at this and all other levels are done by this area of the business vertical.

Memberships are the very fuel with which Netflix works. Anything that can increase memberships or subscriptions are managed and promoted by this business vertical. This includes marketing, sign up prompts, pricing and even partnering with other companies for promotions. They manage and handle all the incomings and welcomings at the Netflix website and app.

A studio is a place where a piece of content is shot. Many of the content that Netflix produces is done in already set up studios. This is also a cost-saving or cutting method . This department works at planning, development, and all the pre and post-production activities for the content. Thus, they work closely with content verticals.

This vertical is focused to spread awareness and promotions about the content that Netflix is producing. This is done through new or traditional media or a combination of both. You must have seen advertisements for Netflix exclusive movies and tv shows, this is the department behind those.

This is the team that ensures the efficient, secure and state of art use of technology tools to manage the whole working of the platform. The data analytics and engineering tools are managed here to provide personalised content to each and every member/subscriber.

Some Facts about Netflix that might Interest you

• Despite more competition, Netflix still has the largest subscriber count in 2020.
• 60 million US adults have a Netflix subscription.
• Netflix was originally called “Kibble”.
• Netflix staffers think that you decide on a movie in two minutes.
• The company is older than most users realise.
• Netflix at its IPO sold its shares about 15 dollars, as its market grew, the share price went up to 350 dollars.
• 41% of Netflix users are watching without paying thanks to password and account sharing.
• Nearly two-thirds of US households now have Netflix.
• Netflix was one of the first streaming services available as an app on different devices.
• You'll Soon be able to Stream Netflix in a Tesla.

Data analytics is the fuel that powers Netflix. Netflix doesn't just work in managing content, movies, TV shows and entertainment but it has a lot of other data to handle as well. There is no efficient way other than “Big data” to handle such enormous amounts of data efficiently.

Netflix does it so well that we do not even notice that change. It cleverly posts content recommendations that are exactly matched with our likes. The data analytics at Netflix brings tailor-made and personalised content to each and every subscriber.

This makes Netflix best not only on the content basis but also on the overall user experience. That is the sole reason why we see steep spikes in Netflix viewerships over the years.

How accurate is the Netflix recommendation system?

Netflix's Recommendation Engine is so accurate that 80% of Netflix viewer activity is driven by personalised recommendations from the engine.

How do I get better recommendations on Netflix?

Whenever you watch a show on Netflix, you can give a thumbs up or thumbs down. Each time you give a show or film a thumbs up, Netflix will likely recommend similar content.

Is Netflix recommendation supervised or unsupervised?

Netflix recommendation engine is a supervised quality control algorithm.

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Libby Plummer

This is how Netflix's top-secret recommendation system works

More than 80 per cent of the TV shows people watch on Netflix are discovered through the platform’s recommendation system. That means the majority of what you decide to watch on Netflix is the result of decisions made by a mysterious, black box of an algorithm. Intrigued? Here's how it works.

Netflix uses machine learning and algorithms to help break viewers’ preconceived notions and find shows that they might not have initially chosen. To do this, it looks at nuanced threads within the content, rather than relying on broad genres to make its predictions. This explains how, for example, one in eight people who watch one of Netflix's Marvel shows are completely new to comic book-based stuff on Netflix.

Read more: Disney's streaming gamble is all about not getting eaten by Netflix

To help understand, consider a three-legged stool. "The three legs of this stool would be Netflix members; taggers who understand everything about the content; and our machine learning algorithms that take all of the data and put things together," says Todd Yellin, Netflix’s vice president of product innovation.

While Netflix has over 100 million users worldwide, if the multiple user profiles for each subscriber are counted, this brings the total to around 250 million active profiles. "What we see from those profiles is the following kinds of data – what people watch, what they watch after, what they watch before, what they watched a year ago, what they’ve watched recently and what time of day". This data forms the first leg of the metaphorical stool.

This information is then combined with more data aimed at understanding the content of shows. The latter – the second leg of the stool – is gathered from dozens of in-house and freelance staff who watch every minute or every show on Netflix and tag it. The tags they use range massively from how cerebral the piece is, to whether it has an ensemble cast, is set in space, or stars a corrupt cop.

"We take all of these tags and the user behaviour data and then we use very sophisticated machine learning algorithms that figure out what’s most important - what should we weigh," Yellin says. "How much should it matter if a consumer watched something yesterday? Should that count twice as much or ten times as much compared to what they watched a whole year ago? How about a month ago? How about if they watched ten minutes of content and abandoned it or they binged through it in two nights? How do we weight all that? That’s where machine learning comes in. What those three things create for us is ‘taste communities’ around the world. It’s about people who watch the same kind of things that you watch."

Viewers fit into multiple taste groups – of which there are "a couple of thousand" – and it’s these that affect what recommendations pop up to the top of your onscreen interface, which genre rows are displayed, and how each row is ordered for each individual viewer. The tags that are used for the machine learning algorithms are the same across the globe. However, a smaller sub-set of tags are used in a more outward-facing way, feeding directly into the user interface and differing depending on country, language and cultural context. "These have to be localised in ways that make sense," Yellin says. "For example, the word ‘gritty’ [as in, 'gritty drama'] may not translate into Spanish or French."

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The data that Netflix feeds into its algorithms can be broken down into two types – implicit and explicit. “Explicit data is what you literally tell us: you give a thumbs up to The Crown , we get it,” Yellin explains. "Implicit data is really behavioural data. You didn’t explicitly tell us 'I liked Unbreakable Kimmy Schmidt ', you just binged on it and watched it in two nights, so we understand that behaviourally. The majority of useful data is implicit."

To illustrate how all this data comes together to help viewers find new things to watch, Netflix looked at the patterns that led viewers towards the Marvel characters that make up The Defenders . While there were some more obvious trends, such as series with strong female leads – like Orange is the New Black – steering characters towards Jessica Jones , there were also a few less obvious sources, like the smart humour of Master of None and the psychological thrill of Making A Murderer driving people towards the wise-ass private detective. Meanwhile, "shows that expose the dark side of society" were shown to drive viewers to Luke Cage , such as the question of guilt in Amanda Knox and the examination of technology in Black Mirror .

This article was originally published by WIRED UK

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A Machine Learning Case Study for Recommendation System of movies based on collaborative filtering and content based filtering.

veeralakrishna/Case-Study-ML-Netflix-Movie-Recommendation-System

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Netflix is all about connecting people to the movies they love. To help customers find those movies, they developed world-class movie recommendation system: CinematchSM. Its job is to predict whether someone will enjoy a movie based on how much they liked or disliked other movies. Netflix use those predictions to make personal movie recommendations based on each customer’s unique tastes. And while Cinematch is doing pretty well, it can always be made better. Now there are a lot of interesting alternative approaches to how Cinematch works that netflix haven’t tried. Some are described in the literature, some aren’t. We’re curious whether any of these can beat Cinematch by making better predictions. Because, frankly, if there is a much better approach it could make a big difference to our customers and our business. Credits: https://www.netflixprize.com/rules.html

Problem Statement

Netflix provided a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.)

• https://www.netflixprize.com/rules.html
• https://www.kaggle.com/netflix-inc/netflix-prize-data
• Netflix blog: https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429
• surprise library: http://surpriselib.com/ (we use many models from this library)
• surprise library doc: http://surprise.readthedocs.io/en/stable/getting_started.html (we use many models from this library)
• installing surprise: https://github.com/NicolasHug/Surprise#installation
• Research paper: http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf (most of our work was inspired by this paper)
• SVD Decomposition : https://www.youtube.com/watch?v=P5mlg91as1c

Real world/Business Objectives and constraints

Objectives:.

• Predict the rating that a user would give to a movie that he has not yet rated.
• Minimize the difference between predicted and actual rating (RMSE and MAPE)

Constraints:

• Some form of interpretability.
• There is no low latency requirement as the recommended movies can be precomputed earlier.

Type of Data:

• There are 17770 unique movie IDs.
• There are 480189 unique user IDs.
• There are ratings. Ratings are on a five star (integral) scale from 1 to 5.
• There is a date on which the movie is watched by the user in the format YYYY-MM-DD.

Getting Started

Start by downloading the project and run "NetflixMoviesRecommendation.ipynb" file in ipython-notebook.

Prerequisites

You need to have installed following softwares and libraries in your machine before running this project.

• Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy, scipy.
• Python 3: https://www.python.org/downloads/
• Anaconda: https://www.anaconda.com/download/
• XGBoost: conda install -c conda-forge xgboost
• Surprise: pip install surprise
• ipython-notebook - Python Text Editor
• sklearn - Machine learning library
• seaborn, matplotlib.pyplot, - Visualization libraries
• numpy, scipy- number python library
• pandas - data handling library
• XGBoost - Used for making regression models
• Surprise - used for making recommendation system models

Veerala Hari Krishna - Complete work

Acknowledgments

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Recommender Systems in Industry: A Netflix Case Study

Cite this chapter.

• Xavier Amatriain 4 , 5 &
• Justin Basilico 4  

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The Netflix Prize put a spotlight on the importance and use of recommender systems in real-world applications. Many the competition provided many lessons about how to approach recommendation and many more have been learned since the Grand Prize was awarded in 2009. The evolution of industrial applications of recommender systems has been driven by the availability of different kinds of user data and the level of interest for the area within the research community. The goal of this chapter is to give an up-to-date overview of recommender systems techniques used in an industrial setting. We will give a high-level description the practical use of recommendation and personalization techniques. We will highlight some of the main lessons learned from the Netflix Prize. We will then use Netflix personalization as a case study to describe several approaches and techniques used in a real-world recommendation system. Finally, we will pinpoint what we see as some promising current research avenues and unsolved problems that deserve attention in this domain from an industry perspective.

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The application of Matrix Factorization to the task of rating prediction closely resembles the technique known as Singular Value Decomposition used, for example, to identify latent factors in Information Retrieval. Therefore, it is common to see people referring to this MF solution as SVD.

For practical purposes we consider responses below a few hundred milliseconds (e.g. 200) to be real-time.

Intermediate recommendations usually represent lists of items that have been pre-selected and even ranked in advanced but need to undergo further processing such as filtering or re-ranking before being presented to the user.

Chukwa is a Hadoop subproject devoted to large-scale log collection and analysis.

Hadoop is an open-source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware.

Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis.

Pig is a high-level platform for creating MapReduce programs used with Hadoop using a language called Pig Latin.

The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware.

Amazon S3 (Simple Storage Service) is an online file storage web service offered by Amazon Web Services.

Apache Kafka is publish-subscribe messaging rethought as a distributed commit log.

Apache Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure.

EVCache is a distributed in-memory data store for the cloud.

MySQL is one of the most popular open source relational databases.

It is important to note that the term “social recommendation” was originally used to describe collaborative filtering approaches [ 7 , 74 ].

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Amatriain, X., Basilico, J. (2015). Recommender Systems in Industry: A Netflix Case Study. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_11

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