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.

What is Problem Formulation in Machine Learning and Top 4 examples of Problem Formulation in Machine Learning?

What are some good datasets for Data Science and Machine Learning?

This scene in the Black Panther trailer, is it T’Challa’s funeral?

r/marvelstudios - This scene in the Black Panther trailer, is it T’Challa’s funeral?

Recommended New Netflix  Movies 2022

  • Hillbilly Effigy author JD Vance now Vice-Presidential candidate. Good Movie to watch
    by /u/redditwastesmyday (Netflix) on July 16, 2024 at 2:49 pm

    I rewatched Hillbilly Effigy after political announcement. It is a very good movie. They NAILED the looks of the actors to the people they were based on. At the very end they show the real people and damn the likeness to the actors is unreal. Now that I have heard some of JD opinions on things, his background gives some understanding to how he reached these opinions. IMO some are skewed by his background. Anyways it is a good movie IMO. One could hope that he would be a champion for poor Appalachia people. ​ submitted by /u/redditwastesmyday [link] [comments]

  • 35 of my fave Horror Comedies (and horror-adjacent comedies)
    by /u/IWishIWasAHorseMan (Movie News and Discussion) on July 16, 2024 at 2:32 pm

    My absolute favourite type of movies are Horror Comedies (and/or Comedy Horrors), and I've spent the last few years obsessively looking for movies in this genre. Edit: This is a list of my 35 favourites, with the earliest from 2004. Disclaimer - I enjoy watching these movies as I find them very entertaining, but I'm not saying that they are all works of art. Some of these lean more towards comedy, and others lean more towards horror. Also, a few movies included could be classified more as Action Comedies or Thriller Comedies, but they evoke the same type of emotions as Horror Comedies. (This is an expansion on my original post from a few weeks ago, which I have deleted to avoid confusion.) The obvious ones: The Cornetto Trilogy - an anthology of unrelated movies directed by Edgar Wright, featuring Simon Pegg and Nick Frost. Shaun of the Dead (2004) - is about zombies, Hot Fuzz (2007) - is about a cult, and The World's End (2013) - is about the apocalypse (what a surprise!). Tucker and Dale vs Evil (2010) - two hillbillies are mistaken for murderers. Then the bodies start piling up. Cabin in the Woods (2011) - a new take on a classic horror concept. Zombieland (2009) - perhaps the most obvious suggestion behind Shaun of the Dead, and rightfully a classic of the genre. The sequel from 2019 is worth a watch, but you know what they say about sequels. My personal faves: School's Out Forever (2021) - Post apocalyptic survival - A student is expelled from a private boarding school, only to return when a mysterious illness starts killing off a majority of the population. There are some aspects of comedy throughout, but it gets really heavy and explores the impact of the situation on the remaining schoolkids and teachers. In a strange way, it reminds me of Lord of the Flies. It flew under the radar when it was released because of the pandemic, which mirrored the challenges faced in the movie. Grabbers (2012) - Alien invasion - When an island off the coast of Ireland is invaded by bloodsucking aliens, the heroes discover that getting drunk is the only way to survive. That description alone should be enough to inspire people to watch it. Full of laughs, great dialogue and (mostly) likeable characters. The aliens were also very well done for a movie with a £4m budget. Get Duked! (2019) - Slasher - Three teenage miscreants are given one last chance to turn their lives around by completing the Duke of Edinburgh Award trek across the highlands. Along for the ride is an overachiever who is in this for the opportunity to pad his CV. However, they are stalked by wealthy aristocrats who intend to hunt and kill them for sport. This movie is quirky in the best of ways, and movie combines the soundtrack in to the story and visuals a lot more than your average horror comedy. However, it wasn't all laughs, with some great unexpected twists and scenes that had me on the edge of my seat wondering what would happen to the characters. Summer of 84 (2018) - 80s Nostalgia Thriller - After suspecting that their police officer neighbor is a serial killer, a group of teenage friends spend their summer spying on him and gathering evidence, but as they get closer to discovering the truth, things get dangerous. This is probably the closest to a true horror movie in this list, but there are still some laughs. The casting feels spot on, the characters are all pretty believable, and who doesn't like some 80's nostalgia. Full of twists and turns, and although there are some questionable decisions made by the main characters, the movie is very entertaining and the pacing is spot on. Detention (2011) - Slasher - As a copycat killer named after movie villain "Cinderhella" stalks the student body at Grizzly Lake High School, a group of co-eds band together to survive while serving detention. I know it sounds like another derivative high school slasher movie, but it most certainly IS NOT. First of all, time-travel. Secondly, did I mention time-travel? This one probably divides opinions the most out of anything listed here, but I think it's pretty much genius. I've heard it described as "Everything Everywhere All at Once" combined with a slasher movie, but I'm not sure if that's accurate because I've never seen EEAaO (I was busy finding horror comedies). The Babysitter (2017) - Satanic cult - When Cole stays up past his bedtime, he discovers that his hot baby sitter belongs to a satanic cult that will stop at nothing to keep him quiet. Great casting, including Samara Weaving and a few faces you might not have expected to see, and generally a very fun movie that keeps you glued to your screen. The sequel from 2020 is also super entertaining, and introduces Jenna Ortega to the cast. Cocaine Bear (2023) - Thriller - An oddball group of cops, criminals, tourists and teens converge on a Georgia forest where a huge black bear goes on a murderous rampage after unintentionally ingesting cocaine. Okay - hear me out! I went in to this with low expectations, and was pleasantly surprised by the cinematography, atmosphere and well-written story. It isn't just a bear running around a forest high on coke. Give it a try. Attack the Block (2011) - Alien invasion - A teen gang in South London defend their block from an alien invasion. Starring pre-Star Wars John Boyega, Attack the Block is a very enjoyable monster movie with a distinct London twist. Well-paced, with the right amount of humour combined with the right amount of action and terror. Happy Death Day (2017) - Slasher - A college student must relive the day of her murder over and over again, in a loop that will end only when she discovers her killer's identity. Think Groundhog Day combined with a slasher. The gradual changes in the 'same' scenes are a fun but effective way of portraying the gradual development of the main character, and the movie is entertaining from start to finish. It's a Wonderful Knife ( 2017) - Slasher - After saving her town from a psychotic killer, Winnie Carruthers' life is less than wonderful. When she wishes she'd never been born, she finds herself in a nightmare parallel universe where without her, things could be much, much worse. You'll either really enjoy this movie and its quirkiness, or you'll hate it - no in between. I loved it, but I won't argue that it's the epitome of cinematography. Boys from County Hell (2020) - Vampires - A crew of hardy road workers, led by a bickering father and son, must survive the night when they accidentally awaken an ancient Irish vampire. The second Irish Horror Comedy on my list (the first being Grabbers), this movie went under the radar for most despite being premiered at the Tribeca Film Festival. A very fun watch, with a twist I certainly didn't expect (I mean in hindsight, I maybe should have, but whatever). Other movies I can't be bothered to expand on: Extracurricular Activities (2019) - A mature, intelligent high school student has a side job arranging "accidental" deaths (no two alike) of fellow students' parents. A cop detective notices this student being connected to all the kids of dead parents. Scouts Guide to the Zombie Apocalypse (2015) - Three scouts, on the eve of their last camp-out, discover the true meaning of friendship when they attempt to save their town from a zombie outbreak. Werewolves Within (2021) - A group of people in a small Vermont town get trapped in a snowstorm only to suspect one of them is a werewolf. Bodies Bodies Bodies (2022) - When a group of rich 20-somethings plan a hurricane party at a remote family mansion, a party game turns deadly. The Blackening (2022) - Seven friends go away for the weekend and end up trapped in a cabin with a killer who has a vendetta. Save Yourselves! (2020) - A couple goes on a retreat and switches off their electronics for the week; the same week the planet comes under attack. Better Watch Out (2016) - On a quiet suburban street, a babysitter must defend a twelve-year-old boy from intruders, only to discover it's far from a normal home invasion. TW: SA. Freaky (2020) - After swapping bodies with a deranged serial killer, a high-school senior discovers that she has fewer than 24 hours before the change becomes permanent. Happy Death Day 2U (2019) - A college student discovers that dying over and over was surprisingly easier than the dangers that lie ahead. The Final Girls (2015) - A young woman grieving the loss of her mother, a famous scream queen from the 1980s, finds herself pulled into the world of her mom's most famous movie. Gatlopp (2022) - A group of old friends reunites for a nostalgic evening of fun and games after a decade apart. After one too many, they decide to play a drinking game, but it's quickly revealed that this game comes with supernatural stakes. Rim of the World (2019) - Four kids at a summer camp are left behind when the camp is evacuated due to an alien invasion. Useless Humans (2020) - A thirtieth birthday and the survival of mankind are left hanging in the balance after a mysterious creature crashes a party, forcing four childhood friends to save the night. The Wolf of Snow Hollow (2020) - Terror grips a small mountain town as bodies are discovered after each full moon. Renfield (2023) - Renfield, Dracula's tortured henchman, is forced to capture prey for his master and do his every bidding. But now, after centuries of servitude, Renfield is ready to see if there is a life outside his boss's shadow. submitted by /u/IWishIWasAHorseMan [link] [comments]

  • BLADE Teaser (2024) With Mahershala Ali & Jennifer Lawrence
    by Friends of Film (Netflix on Medium) on July 16, 2024 at 2:30 pm

    🎬Watch trailer:Continue reading on Medium »

  • Chad Daniels: Empty Nester - a new standup comedy special
    by /u/IsThereADog (Netflix) on July 16, 2024 at 2:29 pm

    first netflix special for a comedian who has been around for a while - i believe this is his 10th hour special he has released he has a great manner with the crowd - can dig himself a hole, and then find his way out - a true professional submitted by /u/IsThereADog [link] [comments]

  • The Mummy Resurrection — Trailer (2025) Dwayne Johnson, Keanu Reeves (HD)
    by Friends of Film (Netflix on Medium) on July 16, 2024 at 2:26 pm

    The Mummy Resurrection — Trailer (2025) 🔔 🔔 🔔 Dwayne Johnson, Keanu Reeves (HD) 🎬Watch trailer:  The prospect of The Mummy 4 with…Continue reading on Medium »

  • Jessica Alba in "Trigger Warning" - Is she entering Steven Seagal territory?
    by /u/-Khlerik- (Netflix) on July 16, 2024 at 2:12 pm

    submitted by /u/-Khlerik- [link] [comments]

  • What is the Best Film You Watched Last Week? (07/09/24 – 07/16/24)
    by /u/ICumCoffee (Movie News and Discussion) on July 16, 2024 at 2:11 pm

    The way this works is that you post a review of the Best Film you watched this week. It can be any new or old release that you want to talk about. Here are some rules: 1. Check to see if your favorite film of last week has been posted already. 2. Please post your favorite film of last week. 3. Explain why you enjoyed your film. 4. ALWAYS use SPOILER TAGS: [Instructions] 5. Best Submissions can display their Letterboxd Accounts the following week. 6. Comments that only contain the title of the film will be removed. ​ Last Week's Best Submissions: Film User / [LB/Web*] Adaptation. (2002) ParaSocialGumShoe Landscape Suicide (1987) [Ako Tao] About Time (2013) [Tim Z] The Bitter Tears of Petra von Kant (1972) [Krios] Dogtown And Z-Boys (2021) FantasticName Chinatown (1974) JinFuu In a Violent Nature (2024) Stewmungous \NOTE: These threads are now posted on Tuesday Mornings]) submitted by /u/ICumCoffee [link] [comments]

  • The Unholy just disappeared
    by /u/EccentricDyslexic (Netflix) on July 16, 2024 at 2:07 pm

    I was watching the unholy last night and wanted to watch the rest of it today, but it’s not in my continue watching list and I did a search for it and it doesn’t seem like I can play it. It’s saying reminder more like this or credits only what gives? submitted by /u/EccentricDyslexic [link] [comments]

  • Official poster for A24 & Aaron Schimberg's 'A Different Man' - Starring Sebastian Stan, Renate Reinsve and Adam Pearson
    by /u/ICumCoffee (Movie News and Discussion) on July 16, 2024 at 1:26 pm

    submitted by /u/ICumCoffee [link] [comments]

  • Discontinuation of Windows APP
    by /u/daffvader (Netflix) on July 16, 2024 at 1:24 pm

    Why? Why would you do this? And now I have to re-login (key in my email address and password) everytime I decide I wanna watch Netflix on my own computer? Wtf Netflix? submitted by /u/daffvader [link] [comments]

  • 夏日咖啡男友(The Boyfriend) EP1–3
    by 蘇峻霆 (Netflix on Medium) on July 16, 2024 at 1:24 pm

    (最後一段有小雷)Continue reading on Medium »

  • A Different Man | Official Trailer HD | A24
    by /u/MarvelsGrantMan136 (Movie News and Discussion) on July 16, 2024 at 12:58 pm

    submitted by /u/MarvelsGrantMan136 [link] [comments]

  • First Poster for Netflix's Musical-Crime-Comedy 'Emilia Perez' - Starring Zoe Saldana, Selena Gomez, and Karla Sofia Gascon - Premiering to rave reviews at Cannes, a lawyer is tasked with assisting an escaped Mexican cartel leader in undergoing sex reassignment surgery to evade the authorities.
    by /u/BunyipPouch (Movie News and Discussion) on July 16, 2024 at 12:57 pm

    submitted by /u/BunyipPouch [link] [comments]

  • Help with JBL 4k Passthrough
    by /u/Legitimate_Peak60 (Netflix) on July 16, 2024 at 12:53 pm

    I currently use google chromecast 4k connected to JBL Bar 500 using their provided hdmi cable then ARC to my 4k Sony TV because its the only way I can play Dolby Atmos. I cant seem to get netflix to play 4k through JBL Bar 500 5.1 passthrough. Someone with this similar problem that they fixed? Yes I am subscribed to 4k on netflix. I made it play 4k when I connected it to the tv, and then connected to soundbar using ARC but whenever I play Dolby Atmos content the sound keeps cutting off and keeps displaying DOLBY ATMOS on the soundbar screen. I have another 2.1 HDMI cable, same thing happens. On both chromecast being connected to the tv and connected to the soundbar. My tv does not have eARC. submitted by /u/Legitimate_Peak60 [link] [comments]

  • Rick and Morty favourite episode?
    by /u/Gnnz (Netflix) on July 16, 2024 at 12:46 pm

    With season 7 out, what is everyone’s favourite episode of this beautiful show? Personally for me it has to be between these two: “The Rickshank Rickdemption” (S3, Episode 1) “Pickle Rick” (S3, Episode 3) submitted by /u/Gnnz [link] [comments]

  • First images from Edward Berger’s “Conclave”
    by /u/KillerCroc1234567 (Movie News and Discussion) on July 16, 2024 at 12:36 pm

    submitted by /u/KillerCroc1234567 [link] [comments]

  • Crime Scene: The Vanishing At The Cecil Hotel
    by /u/peniswhoretheclown (Netflix) on July 16, 2024 at 12:26 pm

    I'm watching this doc as I write this and obviously it's not the best one on Elisa Lam's case. Plus, as most people have come to believe it suggests that she simply had a psychotic break leading her to do what she did either with the intent of dying or not. I don't know what my personal opinion is, honestly. I get why most people have their respective opinions. However, did anyone else just a very odd and off-putting feeling from hotel manager Amy Price? Not just the look on her face or how she talks, but the things she said. "Before I called the police, I called my mother. I told her something really bad had happened and to get ready." Why? Why would you do that? Am I missing something? Does her mom own the hotel or also work their? Also it's just kinda disrespectful and odd to call someone who isn't police first regardless. I also think she's just oddly defensive sounding sometimes, kinda like a toddler who's in trouble. Then again maybe I'm just overthinking nothing. I'm not trying to accuse this random woman or whatever, I'd just like to know if anyone else found that kinda weird. submitted by /u/peniswhoretheclown [link] [comments]

  • Looking for comedies about bachelorette parties and weddings
    by /u/AmIn1amh (Movie News and Discussion) on July 16, 2024 at 12:07 pm

    I’m watching a YouTube video on bridezillas and thought a movie about the craziness around a wedding would be a fun watch. Cause lemme tell you, some of those real stories wouldn’t be out of place as comedy scripts. I’m definitely never getting married🫠😂 An example would be A Rough Night starring Scarlett Johansson submitted by /u/AmIn1amh [link] [comments]

  • Guys suggest me some good series
    by /u/Flaky_Appointment_82 (Netflix) on July 16, 2024 at 12:00 pm

    I really want to watch some series and I am confused..., what should be the best series involving adventure and thrill!? Any suggestions would be helpful..!! submitted by /u/Flaky_Appointment_82 [link] [comments]

  • PlN required each time for R21 titles ?
    by /u/johndoe1985 (Netflix) on July 16, 2024 at 11:30 am

    How can I disable the requirement to enter my PIN each time I watch an R21 movie on the Netflix app? Seems to have started recently I went into security settings and have played around but couldn’t find an option to disable that on my profile submitted by /u/johndoe1985 [link] [comments]

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