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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.

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.

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.
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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 are some good datasets for Data Science and Machine Learning?
This scene in the Black Panther trailer, is it T’Challa’s funeral?
Recommended New Netflix Movies 2022
- Dubs in certain countriesby /u/AsparagusStrong860 (Netflix) on February 13, 2025 at 3:23 am
Why are some dubs available in some countries and in another country it's not there? In Mexico I was able to watch One Piece dubbed in Spanish but in the U.S. It's not there. submitted by /u/AsparagusStrong860 [link] [comments]
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I have the right subscription, but when I play rick and morty and check the resolution it says 1080p. My TV also supports all features according to the app. Also does anyone have any recommendations for shows/movies in 4k? submitted by /u/Previous_Reason7022 [link] [comments]
- Kinda Pregnantby /u/Cold_Reference_3497 (Netflix) on February 13, 2025 at 12:38 am
I’m ngl it’s not like I expected something amazing and original from an Amy Schumer movie but when I tell you this movie is a carbon copy of Lindsay Lohan’s Labor Pains from the early 2000’s it’s insane 😭 don’t get me wrong I know this shit happens all the time but they’re so similar it’s like atp use the same name and call it a remake. submitted by /u/Cold_Reference_3497 [link] [comments]
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Anyone watch prison cell 211? Please no spoilers. Was it good and worth watching? What other shows it kinda like? I have a list of shows to watch on Netflix and trying to decide what order to watch them in. My other shows in my list are: the chestnut man, the night agent, the show girl, money heist, and envious. Thank you for any info! submitted by /u/WeAreHeroes22 [link] [comments]
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I just watched the witcher sirens of the deep movie and while Essi was talking to Geralt about songs written about him, she mentioned a song about him hunting vampires with three stakes and one of the stakes is in his mouth. I might be reaching here but could it be a reference to Zoro from One Piece? as he holds three swords in that style. submitted by /u/InSideTheBoxDamnIt [link] [comments]
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I live in germany, but i watch all my shows and movies in english. My display settings and regular subtitle and audio settings are set to english. The only thing i can’t change is my location. Still, for certain shows, netflix will not show me the english subtitles or audio even tho i KNOW it exists. Pls pls pls tell me theres a way to fix this i will genuinely rip out my hair if there isnt submitted by /u/ur_local_ocd_gae [link] [comments]
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When I go to their site, all they will show is a handful of "trending" movies. If possible, I do not want to subscribe only to find out I didn't want to subscribe. submitted by /u/Jay-jay1 [link] [comments]
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guys im tweaking out my icon was viktor and i just opened my netflix to see it's GONE from my icon AND the arcane section was removed... why do they do this. please if anyone would be so kind to spare me their viktor icon id or any of the other characters i really don't mind submitted by /u/Dramatic-Simple2783 [link] [comments]
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Captain America: Brave New World - Review Thread Rotten Tomatoes: 47% (70 Reviews) Metacritic: 44 (35 Reviews) Reviews: Deadline: Director Julius Onah (Luce) and a boatload of writers provide plenty of oppotunity for Mackie to show his strengths although Evans’ Steve Rogers is a tough act to follow. That fact is even alluded to at one point, but watching Mackie taking Sam Wilson into the big leagues is a game effort with room to grow. Variety (70): Wilson’s Captain America lacks the serum-enhanced invincibility that defined Rogers. He’s a hand-to-hand combat badass, but far more dependent on his shield and wingsuit, both of which are made of vibranium. You could say that that makes him a hero more comparable to, say, Iron Man (though Tony Stark’s principal weapon was Robert Downey Jr.’s motormouth), and Wilson’s all-too-mortal quality comes through in the sly doggedness of Mackie’s when-you’re-number-two-you-try-harder performance. 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This is a film where the President of the United States literally turns red and tries to publicly murder a Black man, and yet according to “Brave New World,” the real problem is that we weren’t sympathetic enough to the dangerously corrupt rage monster. This film’s steadfast refusal to engage with its own ideas, either by artistic design or corporate mandate, reeks of timidity. IndieWire (C-): It’s fitting enough that “Brave New World” is a film about (and malformed by) the pressures of restoring a diminished brand. It’s even more fitting that it’s also a film about the futility of trying to embody an ideal that the world has outgrown. Sam Wilson might find a way to step out of Steve Rogers’ shadow, but there’s still no indication that the MCU ever will. IGN (5/10): Captain America: Brave New World feels neither brave, nor all that new, falling short of strong performances from Anthony Mackie, Harrison Ford, and Carl Lumbly. 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Collider (4/10): In trying to do so much all at once, Captain America: Brave New World forgets what made its title character a relatable fan-favorite. Instead, we get a narrative that is as convoluted as it is boring, visuals that are as unappealing as they are uninspired, and a Marvel movie that is as frustrating as it is forgettable. Had this been a random C-list Marvel hero, that would be forgivable, but for a character as revered as Captain America, it's a huge disappointment. The Guardian (2/5): Brave it might be, but there’s nothing all that “new” about the world revealed in this latest tired and uninspired dollop of content from the Marvel Cinematic Universe. ------------------- Directed by Julius Onah: Following the election of Thaddeus Ross as the president of the United States, Sam Wilson finds himself at the center of an international incident and must work to stop the true masterminds behind it. Cast: Anthony Mackie as Sam Wilson / Captain America Danny Ramirez as Joaquin Torres / Falcon Shira Haas as Ruth Bat-Seraph Carl Lumbly as Isaiah Bradley Xosha Roquemore as Leila Taylor Jóhannes Haukur Jóhannesson as Copperhead Giancarlo Esposito as Seth Voelker / Sidewinder Tim Blake Nelson as Samuel Sterns / Leader Harrison Ford as Thaddeus "Thunderbolt" Ross / Red Hulk submitted by /u/MarvelsGrantMan136 [link] [comments]
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I'm getting this message on my desktop PC that's hard-wired into my home network, for my account, that I pay for. I also turned my wifi off just to make sure. My device's gateway is 10.0.0.1, which is the IP of my comcast modem. There is an option to watch temporarily., but how do I fix this permanently? submitted by /u/TaraBryn [link] [comments]
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List of Freely available programming books - What is the single most influential book every Programmers should read
- Bjarne Stroustrup - The C++ Programming Language
- Brian W. Kernighan, Rob Pike - The Practice of Programming
- Donald Knuth - The Art of Computer Programming
- Ellen Ullman - Close to the Machine
- Ellis Horowitz - Fundamentals of Computer Algorithms
- Eric Raymond - The Art of Unix Programming
- Gerald M. Weinberg - The Psychology of Computer Programming
- James Gosling - The Java Programming Language
- Joel Spolsky - The Best Software Writing I
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- Structure and Interpretation of Computer Programs
- The C Programming Language by Kernighan and Ritchie
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- The Mythical Man Month
- The Art of Computer Programming by Donald Knuth
- Compilers: Principles, Techniques and Tools by Alfred V. Aho, Ravi Sethi and Jeffrey D. Ullman
- Gödel, Escher, Bach by Douglas Hofstadter
- Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
- Effective C++
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- CODE by Charles Petzold
- Programming Pearls by Jon Bentley
- Working Effectively with Legacy Code by Michael C. Feathers
- Peopleware by Demarco and Lister
- Coders at Work by Peter Seibel
- Surely You're Joking, Mr. Feynman!
- Effective Java 2nd edition
- Patterns of Enterprise Application Architecture by Martin Fowler
- The Little Schemer
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- Why's (Poignant) Guide to Ruby
- The Inmates Are Running The Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity
- The Art of Unix Programming
- Test-Driven Development: By Example by Kent Beck
- Practices of an Agile Developer
- Don't Make Me Think
- Agile Software Development, Principles, Patterns, and Practices by Robert C. Martin
- Domain Driven Designs by Eric Evans
- The Design of Everyday Things by Donald Norman
- Modern C++ Design by Andrei Alexandrescu
- Best Software Writing I by Joel Spolsky
- The Practice of Programming by Kernighan and Pike
- Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt
- Software Estimation: Demystifying the Black Art by Steve McConnel
- The Passionate Programmer (My Job Went To India) by Chad Fowler
- Hackers: Heroes of the Computer Revolution
- Algorithms + Data Structures = Programs
- Writing Solid Code
- JavaScript - The Good Parts
- Getting Real by 37 Signals
- Foundations of Programming by Karl Seguin
- Computer Graphics: Principles and Practice in C (2nd Edition)
- Thinking in Java by Bruce Eckel
- The Elements of Computing Systems
- Refactoring to Patterns by Joshua Kerievsky
- Modern Operating Systems by Andrew S. Tanenbaum
- The Annotated Turing
- Things That Make Us Smart by Donald Norman
- The Timeless Way of Building by Christopher Alexander
- The Deadline: A Novel About Project Management by Tom DeMarco
- The C++ Programming Language (3rd edition) by Stroustrup
- Patterns of Enterprise Application Architecture
- Computer Systems - A Programmer's Perspective
- Agile Principles, Patterns, and Practices in C# by Robert C. Martin
- Growing Object-Oriented Software, Guided by Tests
- Framework Design Guidelines by Brad Abrams
- Object Thinking by Dr. David West
- Advanced Programming in the UNIX Environment by W. Richard Stevens
- Hackers and Painters: Big Ideas from the Computer Age
- The Soul of a New Machine by Tracy Kidder
- CLR via C# by Jeffrey Richter
- The Timeless Way of Building by Christopher Alexander
- Design Patterns in C# by Steve Metsker
- Alice in Wonderland by Lewis Carol
- Zen and the Art of Motorcycle Maintenance by Robert M. Pirsig
- About Face - The Essentials of Interaction Design
- Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky
- The Tao of Programming
- Computational Beauty of Nature
- Writing Solid Code by Steve Maguire
- Philip and Alex's Guide to Web Publishing
- Object-Oriented Analysis and Design with Applications by Grady Booch
- Effective Java by Joshua Bloch
- Computability by N. J. Cutland
- Masterminds of Programming
- The Tao Te Ching
- The Productive Programmer
- The Art of Deception by Kevin Mitnick
- The Career Programmer: Guerilla Tactics for an Imperfect World by Christopher Duncan
- Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp
- Masters of Doom
- Pragmatic Unit Testing in C# with NUnit by Andy Hunt and Dave Thomas with Matt Hargett
- How To Solve It by George Polya
- The Alchemist by Paulo Coelho
- Smalltalk-80: The Language and its Implementation
- Writing Secure Code (2nd Edition) by Michael Howard
- Introduction to Functional Programming by Philip Wadler and Richard Bird
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Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.
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submitted by /u/Odd_Advance_6438 [link] [comments]
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- TIL there was a confidential Simple Sabotage directive for CIA workers to use in case of a military coup in WWII.by /u/AriadneThread on February 13, 2025 at 2:24 am
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- TIL about the West Point Spiked Eggnog Riot of 1826, when cadets, including future Confederate President Jefferson Davis, went on a wild bender after sneaking booze into the academy. Captain Hitchcock tried to regain control but was attacked, and nearly shot, while a drunk drum corps played outsideby /u/datcraybetch on February 12, 2025 at 11:42 pm
submitted by /u/datcraybetch [link] [comments]
- TIL that 40% of the recruits in the Red Army in the 1920s had whip marks on their backs, a punishment used by boyars and landlords for rebellions caused by hunger.by /u/Conscious-Ad-7656 on February 12, 2025 at 11:32 pm
submitted by /u/Conscious-Ad-7656 [link] [comments]
Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.
- Mars is Rocked By Powerful Quakes That Don't Always Begin Under The Surface, Research Showsby /u/sciencealert on February 12, 2025 at 10:56 pm
submitted by /u/sciencealert [link] [comments]
- Sea turtles learn and remember Earth's magnetic field to navigate, but RF signals disrupt their compass, affecting migration patterns.by /u/sibun_rath on February 12, 2025 at 8:30 pm
submitted by /u/sibun_rath [link] [comments]
- A new study analyzing Long COVID healthcare utilization in Colorado reveals a significant shift from acute care to outpatient services following diagnosis, shedding light on evolving treatment patterns and the broader healthcare burden posed by the condition.by /u/CUAnschutzMed on February 12, 2025 at 8:30 pm
submitted by /u/CUAnschutzMed [link] [comments]
- After Elon Musk took over, hate speech increased on X (Twitter). The weekly rate of hate speech increased by about 50%, including increased use of specific homophobic, transphobic, and racist slurs. The average number of 'likes' on hate posts also increased by 70%.by /u/mvea on February 12, 2025 at 8:28 pm
submitted by /u/mvea [link] [comments]
- The James Webb Space Telescope provides an unprecedented view into the PDS 70 system; new images provide direct evidence that the planets are still growing and competing with their host star for material, supporting the idea that planets form through a process of 'accretion'.by /u/UVicScience on February 12, 2025 at 7:34 pm
submitted by /u/UVicScience [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- Four Nations: Mitch Marner rips one past Gustavsson in overtime as Canada defeats Sweden in the opening gameby /u/SkepticalZebra on February 13, 2025 at 4:15 am
submitted by /u/SkepticalZebra [link] [comments]
- Chase Briscoe gives Toyota its first Daytona 500 pole as Johnson and Truex also lock into NASCAR openerby /u/Oldtimer_2 on February 13, 2025 at 4:12 am
submitted by /u/Oldtimer_2 [link] [comments]
- Alex Bregman to sign with Red Sox: Star third baseman agrees to three-year, $120 million dealby /u/Oldtimer_2 on February 13, 2025 at 3:44 am
submitted by /u/Oldtimer_2 [link] [comments]
- Report: Padres, RHP Nick Pivetta agree to 4-year, $55M dealby /u/Oldtimer_2 on February 13, 2025 at 3:04 am
submitted by /u/Oldtimer_2 [link] [comments]
- 4 Nations: Dahlin saves a seemingly guaranteed goal, Sweden staying alive against Canadaby /u/SkepticalZebra on February 13, 2025 at 2:56 am
submitted by /u/SkepticalZebra [link] [comments]