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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?

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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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    Yep, as the title reads, everyone walked out and there was a line of refunds given by the manager. Warning (no spoilers): there should be a major strobe/ seizure warning for this film. This film has already been revoked from all near by major movie theatres. I could only find one playing this film and now I know why. The strobe/flashing effect in this film is constant and intrusive. I’m not sensitive and felt very ill after watching only 40 mins of this movie. This movie was a abysmal and atrocious. The acting was even more horrific. The videography was a combination of lifetime movie and shaky hands with a swooping action. I got so sick I had to shut my eyes. This is no exaggeration as others in the theatre begun to feel ill from the repetitive flashing and strobing lights to create a terrible aesthetic of horror (think bad Halloween display with a $5 strobe light and a werewolf costume lurking in the shadows.) What fuckery. The effort was beyond corny and cheesy and was just appalling. I’ll preface I can appreciate a genuine cheesy horror film but this was bitter and in poor taste. I know it was a low budget film but there was no attempt to make this enjoyable. I will say I like the werewolf design but you couldn’t see anything past the strobing effect that wouldn’t stop for 40 minutes. Save your self and your money please. If an entire room of people walked out that should be enough said. I won’t be surprised if the movie gets removed from all theatres. submitted by /u/raindroplets99 [link] [comments]

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    In the opening scene of Inglourious Basterds, the farmer LaPadite endures a surprise visit from Hans Landa. A few minutes into the scene, LaPadite asks one of his daughters to close the window of the farmhouse, which she immediately does. This moment is visible at the 3:31 mark in this video. Why does LaPadite ask for the window to be closed? I had thought that having the window closed might make it easier for those hiding below the floorboards to hear the conversation, to alert them of any danger. It's also possible that he doesn't want the German soldiers outside to hear any conversation, but they are standing very far away. I really don't know. The whole scene is so carefully designed that I know there must have been some thought behind including this moment. submitted by /u/cmm8228 [link] [comments]

  • Official Discussion Megathread (Y2K / Werewolves / The Return / Nutcrackers)
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    Newly Released Movies Y2K Werewolves The Return Streaming Releases Nutcrackers Still in Theaters Wicked: Part 1 Gladiator II Moana 2 submitted by /u/LiteraryBoner [link] [comments]

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    Poll If you've seen the film, please rate it at this poll If you haven't seen the film but would like to see the result of the poll click here Rankings Click here to see the rankings of 2024 films Click here to see the rankings for every poll done Summary: In the most unlikely of places, four siblings find a loving shelter in an unexpected turn of circumstances. This endearing comedy-drama draws inspiration from actual events and deftly crafts a gripping story that unites everyone. Director: David Gordon Green Writers: Leland Douglas Cast: Ben Stiller as Mike Maxwell Homer Janson as Justice Kicklighter Ulysses Janson as Junior Kicklighter Arlo Janson as Simon Kicklighter Atlas Janson as Samual Kicklighter Linda Cardellini as Gretchen Rice Toby Huss as Al Wilmington Rotten Tomatoes: 37% Metacritic: 54 VOD: Hulu submitted by /u/LiteraryBoner [link] [comments]

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    Poll If you've seen the film, please rate it at this poll If you haven't seen the film but would like to see the result of the poll click here Rankings Click here to see the rankings of 2024 films Click here to see the rankings for every poll done Summary: Two scientists try to stop a mutation that turns people into werewolves after being touched by a super-moon the year before. Director: Steven C. Miller Writers: Matthew Kennedy Cast: Frank Grillo as Wesley Katrina Law as Amy Ilfenesh Hadera as Lucy James Michael Cummings as Cody Lou Diamond Phillips as Dr. Aranda Kamdyn Gary as Emma Rotten Tomatoes: TBD Metacritic: TBD VOD: Theaters submitted by /u/LiteraryBoner [link] [comments]

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    Poll If you've seen the film, please rate it at this poll If you haven't seen the film but would like to see the result of the poll click here Rankings Click here to see the rankings of 2024 films Click here to see the rankings for every poll done Summary: Two high school nobodies make the decision to crash the last major celebration before the new millennium on New Year's Eve 1999. The night becomes even crazier than they could have ever dreamed when the clock strikes midnight. Director: Kyle Mooney Writers: Kyle Mooney, Evan Winter Cast: Jaeden Martell as Eli Rachel Zegler as Laura Julian Dennison as Danny Daniel Zolghadri as CJ Lachlan Watson as Ash Fred Durst as Fred Durst Kyle Mooney as Garrett Rotten Tomatoes: 72% Metacritic: 52 VOD: Theaters submitted by /u/LiteraryBoner [link] [comments]

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    Hi, I've been experiencing problems when trying to watch anything on Netflix. It's just a black screen with the video audio. I use a MacBook and watch on Google Chrome. I've tried using Safari but it's the same thing. help please! submitted by /u/Loud_Opinion230 [link] [comments]

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    Keep an eye on your netflix account. I just recently noticed a netflix monthly charge had gone from basic service at approx $8 to $43. Went in account and discovered scammers had upgraded my account to premium and added two members at $8 each with bogus email addresses. This had actually been happening for the last 5 months and cuz payments are auto deduct I really didn't notice until recently. I contacted Netflix and was told they could only refund the last month of overcharges. My response to that was to cancel the account. submitted by /u/Playful_Disaster3227 [link] [comments]

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    If you're a movie buff like me, you've seen the majority if not all of Arnold's movies. I've seen the vast majority of them. I just finished watching Raw Deal. It was pretty bad from start to finish. Most of his movies have some redeeming value - great catch phrases, bad ass action sequences, etc. This had none of that. What is your least favorite Arnold movie and why? submitted by /u/Madiconsin73 [link] [comments]

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    Hello, i cant get my netflix to play 4K. 1) Dell G3223Q 4K HDR + Displayport 1.4, HDCP 2.2 support (both cable and monitor) 2) i5-14600KF + RTX 3080 3) Win 11 + HEVC support installed 4) obviously Netflix premium plan 5) 2700 mbps symetric internet line (effectively limited to 2500mbps by motherboard, 500mbps symetric DEDICATED TO ME, rest shared) ->> no doubt about speed 6) tried using edge or netflix app, still getting 1080p 7) in-app settings are set to highest possible 8) HDR enabled/disabled tried both 9) refreshrate 144/120/60 Hz tried 10) secondary 1080p screen disconnected Is there anything else i can do? Still cant figure out why am i not playing 4K.... submitted by /u/Tydli [link] [comments]

  • What's the last movie you couldn't finish?
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    Now, when i start a movie almost 100% of the time i just finish it, struggle to the end but some time ago i was thinking and my brain was like: 'hey, remember that Death Note movie netflix did? Let's watch it, it can't be that bad and if it's THAT bad we at least can have some fun' and jesus christ, i just turned it off, even Margaret wasn't enough for me to finish it. submitted by /u/AdrianVeidt19 [link] [comments]

  • Netflix’s ‘The Diplomat’: Is Scotland Really the Crux of U.S. Naval Security?
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    I’m sure this has been brought up before but there is still a problem with going from one episode to the next and then the picture going blurry. Once you back out or restart the app, the picture is once again clear. I’m watching on the newest ATV with 500mb + wired internet. Does anyone know why this happens and is there a fix ? Is it Netflix or Apple ? This has been like this for quite awhile now. Real frustrating. Thanks. submitted by /u/kheifert1 [link] [comments]

  • Crunchyroll’s Upward Momentum…
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    Crunchyroll has experienced tremendous growth…but a new era is on the horizon.Continue reading on Medium »

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