What is machine learning and how does Netflix use it for its recommendation engine?

What is machine learning and how does Netflix use it for its recommendation engine?

What is machine learning and how does Netflix use it for its recommendation engine?

What is an online recommendation engine?

Think about examples of machine learning you may have encountered in the past such as a website like Netflix  that recommends what video you may be interested in watching next?
Are the recommendations ever wrong or unfair? We will  give an example and explain how this could be addressed.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

Machine learning is a field of artificial intelligence that Netflix uses to create its recommendation algorithm. The goal of machine learning is to teach computers to learn from data and make predictions based on that data. To do this, Netflix employs Machine Learning Engineers, Data Scientists, and software developers to design and build algorithms that can automatically improve over time. The Netflix recommendations engine is just one example of how machine learning can be used to improve the user experience. By understanding what users watch and why, the recommendations engine can provide tailored suggestions that help users find new shows and movies to enjoy. Machine learning is also used for other Netflix features, such as predicting which shows a user might be interested in watching next, or detecting inappropriate content. In a world where data is becoming increasingly important, machine learning will continue to play a vital role in helping Netflix deliver a great experience to its users.

What is machine learning and how does Netflix use it for its recommendation engine?
What is machine learning and how does Netflix use it for its recommendation engine?

Netflix’s recommendation engine is one of the company’s most valuable assets. By using machine learning, Netflix is able to constantly improve its recommendations for each individual user.

Machine learning engineers, data scientists, and developers work together to build and improve the recommendation engine.

  • They start by collecting data on what users watch and how they interact with the Netflix interface.
  • This data is then used to train machine learning models.
  • The models are constantly being tweaked and improved by the team of engineers.
  • The goal is to make sure that each user sees recommendations that are highly relevant to their interests.

Thanks to the work of the team, Netflix’s recommendation engine is constantly getting better at understanding each individual user.

How Does It Work?

In short, Netflix’s recommendation algorithm looks at what you’ve watched in the past and then makes recommendations based on that data. But of course, it’s a bit more complicated than that. The algorithm also looks at data from other users with similar watching habits to yours. This allows Netflix to give you more tailored recommendations.

For example, say you’re a big fan of Friends (who isn’t?). The algorithm knows that a lot of Friends fans also like shows like Cheers, Seinfeld, and The Office. So, if you’re ever feeling nostalgic and in the mood for a sitcom marathon, Netflix will be there to help you out.

But That’s Not All…

Not only does the algorithm take into account what you’ve watched in the past, but it also looks at what you’re currently watching. For example, let’s say you’re halfway through Season 2 of Breaking Bad and you decide to take a break for a few days. When you come back and finish Season 2, the algorithm knows that you’re now interested in similar shows like Dexter and The Wire. And voila! Those shows will now be recommended to you.

Of course, the algorithm isn’t perfect. There are always going to be times when it recommends a show or movie that just doesn’t interest you. But hey, that’s why they have the “thumbs up/thumbs down” feature. Just give those shows the old thumbs down and never think about them again! Problem solved.

Another angle :

When it comes to TV and movie recommendations, there are two main types of data that are being collected and analyzed:

1) demographic data

2) viewing data.

Demographic data is information like your age, gender, location, etc. This data is generally used to group people with similar interests together so that they can be served more targeted recommendations. For example, if you’re a 25-year-old female living in Los Angeles, you might be grouped together with other 25-year-old females living in Los Angeles who have similar viewing habits as you.

Viewing data is exactly what it sounds like—it’s information on what TV shows and movies you’ve watched in the past. This data is used to identify patterns in your viewing habits so that the algorithm can make better recommendations on what you might want to watch next. For example, if you’ve watched a lot of romantic comedies in the past, the algorithm might recommend other romantic comedies that you might like based on those patterns.

Are the Recommendations Ever Wrong or Unfair?
Yes and no. The fact of the matter is that no algorithm is perfect—there will always be some error involved. However, these errors are usually minor and don’t have a major impact on our lives. In fact, we often don’t even notice them!

The bigger issue with machine learning isn’t inaccuracy; it’s bias. Because algorithms are designed by humans, they often contain human biases that can seep into the recommendations they make. For example, a recent study found that Amazon’s algorithms were biased against women authors because the majority of book purchases on the site were made by men. As a result, Amazon’s algorithms were more likely to recommend books written by men over books written by women—regardless of quality or popularity.

These sorts of biases can have major impacts on our lives because they can dictate what we see and don’t see online. If we’re only seeing content that reflects our own biases back at us, we’re not getting a well-rounded view of the world—and that can have serious implications for both our personal lives and society as a whole.

One of the benefits of machine learning is that it can help us make better decisions. For example, if you’re trying to decide what movie to watch on Netflix, the site will use your past viewing history to recommend movies that you might like. This is possible because machine learning algorithms are able to identify patterns in data.

Another benefit of machine learning is that it can help us automate tasks. For example, if you’re a cashier and have to scan the barcodes of the items someone is buying, a machine learning algorithm can be used to automatically scan the barcodes and calculate the total cost of the purchase. This can save time and increase efficiency.

The Consequences of Machine Learning

While machine learning can be beneficial, there are also some potential consequences that should be considered. One consequence is that machine learning algorithms can perpetuate bias. For example, if you’re using a machine learning algorithm to recommend movies to people on Netflix, the algorithm might only recommend movies that are similar to ones that people have already watched. This could lead to people only watching movies that confirm their existing beliefs instead of challenged them.

Another consequence of machine learning is that it can be difficult to understand how the algorithms work. This is because the algorithms are usually created by trained experts and then fine-tuned through trial and error. As a result, regular people often don’t know how or why certain decisions are being made by machines. This lack of transparency can lead to mistrust and frustration.

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    im sitting here in germany vibing out to the old ice cube stuff and then i want to watch friday and guess what we dont have it here in germany on netflix...please tell me how my homeboy and me can watch that movie and get the feeling from when we were young? use a VPN? or how the hell can we watch that movie without buying it on amazon? submitted by /u/Chemical_Delay_3504 [link] [comments]

  • I'm trying to find a comedians Netflix special in which he makes a joke about eating chicken wings and not being allowed to have another one until he has cleaned the bone he says "I will decimate a wing"
    by /u/Advanced_Newspaper_1 (Netflix) on July 19, 2024 at 10:23 pm

    I can only remember the part where he says he will decimate a wing. I wish I knew more I have no idea who the comedian is and it's really annoying me can anyone help? submitted by /u/Advanced_Newspaper_1 [link] [comments]

  • a serie to advise me ?
    by /u/Kailingoff067 (Netflix) on July 19, 2024 at 8:56 pm

    hi ,i am looking for a new serie to watch on netflix, i did enjoyed breaking bad, better call saul and the movie. i didnt watched a lot of series, but peaky blinders semmed to be great. i like action, (basic lol) Any advises ? submitted by /u/Kailingoff067 [link] [comments]

  • Netflix removed my projector from compatible devices
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    Hi all, i am using Yeefon brand projector from Amazon since 3 months which had Netflix installed. I was happily using it like a normal TV only to my surprise since 15th July it shows me E17 error saying no longer supports the device. What to do? I tried updating my projector and netflix. Projector link in comments submitted by /u/Used_Raisin_7847 [link] [comments]

  • Colour settings question
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  • New UI - Team Terrible
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    I so hate this new UI. They made it look like Hulu. It was nice being able to see a few things in a row to quickly browse. Shmazier isn’t always better. submitted by /u/Out_of_ughs [link] [comments]

  • Suggest the best show
    by /u/snugglebunnnny (Netflix) on July 19, 2024 at 8:10 pm

    Suggest me the most tempting show from these options . I made a list through recommendations in this sub as per my taste and must watch shows . I haven’t watch many famous shows . I was into kdramas previously. I love shows of every genre but not the ones in which I don’t know what is happening. I don’t enjoy weird humor . The character is being weird . I don’t find it funny . But I like it when character gets into a weird situation . ( I can’t explain better I’m sorrryyyy) I love heartwarming shows too which give me the feels , the butterflies . Time travel ones are awesome and interesting . As I said I love every genre. Romance as a subplot is something I enjoy but not a must requirement. I love romantic shows but most are too cringey, some are too complicated . I love serial killers shows, the suspense drives me crazy but they get too draggy or slow , so tough to find a good one . I love simple light hearted shows too , but they are hard to get into sometimes. My top shows are : - moon lovers Mouse Queen inhyun’s man / queen and I Weak hero class 1 Queen charlotte: A Bridgerton Story Queen of tears Twinkling watermelon Mr queen Normal people Crash landing on you Shows I dropped : - crazy ex girlfriend Lucifer Sex education Young royals Midnight at the pera place The show must be really catchy that I’ll watch whole day and night because it’s just too good to stop and take a break . Which one to watch first : The last kingdom What we do in the shadows (only watched movie ,not that tempting I want) Anne with an E. The last of us(watched an episode , almost got hooked but not in the mood for this complicated show , I love tension so may be I’ll watch this again some day ) Outlander Dark ( shows like this take time to get into , idk watched episode 1 only ) Hart of dixie Jane the virgin Superstore Mr inbetween The girl from nowhere Ozark Six feet under True blood Love Death Robot Bloodline Shadow and bone Queens Gambit Chernobyl Mindhunter The Americans Midnight mass Behind her eyes The Haunting of Hill House Heart stopper Midnight at the pera place Blue eye samurai Elite ( season 1 to 3) it has my interest but heard only season 1 to 3 are good rest aren’t worth watching . Is it okay if I watched 3 seasons only . The magicians Orphan black Wolf hall Castlevania submitted by /u/snugglebunnnny [link] [comments]

  • new to america, shows and movies to watch
    by /u/Forward-Budget7747 (Netflix) on July 19, 2024 at 7:56 pm

    hey guys, i have a cousin that's new to america and want to show him some movies and shows that american kids (born around 2006) have grown up watching. i have a few but i want to make sure i don't miss anything since i didn't watch a lot growing up for some reason lol. let me know all the movies and tv shows that "everyone" has watched. thanks! submitted by /u/Forward-Budget7747 [link] [comments]

  • The daredevils of Netflix's stomach-turning 'Skywalkers' reveal their biggest fears
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  • I’m feel empty after finishing a great show, what should I do?
    by /u/Pancakefan16 (Netflix) on July 19, 2024 at 6:29 pm

    I don’t know where to post this so I’ll post this here. I just finished watching a great Netflix show (A Series of Unfortunate Events) and I feel sad, and empty. I have been watching it for 3 days and just finished it today, the ending made me cry, I feel like it wasn’t a good enough ending for that good of a show, it wasn’t the “happy ending” I thought it was gonna be after the sad story. Part of the reason I feel so empty is because, I have seen the show before but I never watched more than the first season , I don’t remember the first time I watched it but I was a bit younger and seeing it again made me remember those times. The show is genuinely amazing, and I had a dream about it (I’m not kidding, I went to sleep after watching some episodes and I had a dream about the main characters) and I can’t move on, I feel I’ll never watch something as great as the show and there’s no replacement, nothing new to watch. What should I do to stop feeling empty and sad that I finished it? submitted by /u/Pancakefan16 [link] [comments]

  • How many ADS are there on the low $6.99 plan?
    by /u/redditwastesmyday (Netflix) on July 19, 2024 at 5:39 pm

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  • Low Bitrade while achieving all requirements
    by /u/Smurfritow (Netflix) on July 19, 2024 at 5:24 pm

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  • Subtitles too dim when lowering phone brightness (duh hear me out)
    by /u/Penny_Royall (Netflix) on July 19, 2024 at 4:29 pm

    So I like to watch a little netflix on my phone before bed, I don't think it's was like this before, but subtitles got dimmed along with the brightness of the show, from white to grey, this makes it really hard to read, as I don't wanna get blinded by my phone in the dark. Anyone know how to fix this? My phone is Samsung S24U btw. submitted by /u/Penny_Royall [link] [comments]

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    Netflix is going to force those with the grandfathered plan (Basic, no ads currently $11.99 per month) to choose from one of its other plans. What will you choose or will you leave? https://finance.yahoo.com/news/netflix-ending-basic-11-99-222921486.html submitted by /u/justmep3 [link] [comments]

  • I need help
    by /u/The_Coolest_Guy123 (Netflix) on July 19, 2024 at 9:40 am

    So I'm just planning on buying a premium Netflix subscription. I had recently learned that Netflix also offers a lot of different games. The one that intrigued me the most surely was Hades. I just wanted to know if it was available in Bulgaria as I would be quite disappointed if it isn't. I deeply believe that you will help me! submitted by /u/The_Coolest_Guy123 [link] [comments]

  • Netflix Beats Subscriber Targets, Cautions On Ad Growth
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    by /u/Adorable-Shoulder976 (Netflix) on July 19, 2024 at 7:49 am

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  • Looking for a show I was very briefly shown
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    Hello everyone, Yesterday on the homepage the big trailer at the top was about a new show released recently that I thought I could watch later but I can't remember its name. Unfortunately I can't remember much: only that the trailer featured a woman in a military compound, the show description was about money and power I guess. I couldn't find it again but thought maybe it would ring a bell for you 🙂 Thanks submitted by /u/ResponsiblePension65 [link] [comments]

  • ‘Old Guard 2’ Post-Production Got ‘Shut Down’ by Netflix, but the Sequel Will Come Out ‘Soon’: ‘I Really Love This Movie’
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  • Arcane temporada 1- Steelbook
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    Arcane, la adaptación del videojuego que nació o en las entrañas de Netflix, ahora lanza su primera temporada en una edición super especial. submitted by /u/Low_Television4489 [link] [comments]

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