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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.
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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.
AI Jobs and Career
And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.
What are some good datasets for Data Science and Machine Learning?
This scene in the Black Panther trailer, is it T’Challa’s funeral?
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Recommended New Netflix Movies 2022
- Why did Richard lure Anna to Lexy Jones house in His & Hers
by /u/baribanana (Netflix) on January 15, 2026 at 2:09 pmI am still confused about Richard's role. His wife is not the killer, nor is he. So why did he lure Anna to the house? Why take her phone? Lexy didn't know Anna was coming and was surprised to see her there. So what did Richards want? submitted by /u/baribanana [link] [comments]
- Hi, Reddit! We’re David Sims and Shirley Li, culture writers at The Atlantic. We’re here to debrief the Golden Globes, discuss the best and worst of releases of 2025 ahead of the Oscars, and talk about the trends and films we’re watching out for in 2026. Ask us anything!
by /u/theatlantic (Movie News and Discussion) on January 15, 2026 at 2:00 pmI, David Sims, write about movies and culture. I’m also the co-host of the Blank Check podcast. I’ve written recently about Timothée Chalamet’s surprising Golden Globes acceptance speech, the apocalyptic potential of the Netflix–Warner Bros. deal, and Mad Men’s HBO Max debut. I’ve recently reviewed Hamnet, Ella McCay, Avatar: Fire and Ash, Marty Supreme and Is This Thing On? I, Shirley Li, write about Hollywood and the shifting culture of the American entertainment industry. I’ve written recently about the significance of the Golden Globes's two big winners, Timothée Chalamet’s promotion of Marty Supreme, and Bowen Yang’s Saturday Night Live career. I’ve recently reviewed Stranger Things, Tim Robinson’s The Chair Company, Die My Love, and Bugonia. We’re looking forward to talking all things film with you all. Ask us anything! submitted by /u/theatlantic [link] [comments]
- What kind of data does Netflix collect on users?by /u/CalebOnPoint (Netflix) on January 15, 2026 at 1:51 pm
Hey guys, So just curious, what kind of data does Netflix collect when you use their streaming platform? Is there some file with all the shows I like with my name on it? And does Netflix sell this to other third-party companies (to target ads to me)? How does that work exactly? Would be interesting for somebody in the company to explain too if possible. Also kinda creepy when Netflix asks me halfway through my programing if "I'm still watching", like wtf?? Also if I pay for the service does this go away? Like I feel like if somebody pays for a service, and that company sells their data that's kinda bullcrap IMO. I'd honestly pay a premium to make sure private stuff like that isn't used against me. Just makes me feel like I'M the product, you know? submitted by /u/CalebOnPoint [link] [comments]
- anyone else liked the adult animated shows arcane and wolf king?by /u/DuxxieDings (Netflix) on January 15, 2026 at 1:45 pm
i loved them! they just the shows that itched my mature show itch, were dark ended grey, scaled, no happy ending for some characters. overall no scared to make people jump or cry. they are how you do adult shows. Jinx's character did have some teenager focused jokes but she was my favorite! submitted by /u/DuxxieDings [link] [comments]
- I noticed something weird about Netflix Korea's biggest reality shows (Physical 100 & Culinary Class Wars), and now I can't unsee itby /u/beezy182 (Netflix) on January 15, 2026 at 1:21 pm
I've been watching a lot of Korean Netflix lately, and something started bugging me (and yes, I used some AI to concise the information to explain my concern). The more I dug into it, the more I realized there's a pattern here that's either the craziest coincidence ever or something deliberate. The Pattern Netflix Korea's two biggest unscripted hits both had Season 2 in 2024-2025. In BOTH seasons, the winner followed the exact same arc: Eliminated from the competition Brought back through a NEW "second chance" mechanic that didn't exist in Season 1 Won the entire season against opponents who had never been eliminated This happened in Physical: 100 Season 2 and Culinary Class Wars Season 2. Two different shows. Two different production companies. Same exact pattern. Physical: 100 Season 2 – Amotti Physical: 100 is basically Squid Game meets CrossFit. 100 people, brutal physical challenges, last one standing wins ₩300 million (~$230K USD). Winner: Amotti (Kim Jae-hong) Quick background: 33-year-old former CrossFit athlete from Daegu. Ranked 6th in the 2019 CrossFit Open, 2nd at the Asia Championship. In January 2021, a car door crushed his ankle while he was riding his scooter. 10-hour surgery. Doctors said he might never walk again. He rebuilt himself as a fitness YouTuber (300K+ subscribers) and became a Lululemon ambassador. Here's where it gets interesting: Amotti was nearly selected for Season 1 but didn't make the final cast. (This is on his Wikipedia page.) Production knew him. Knew his story. Said no. Season 2: He gets in, competes, and gets eliminated in Quest 2 (Maze Race). Pretty early. But wait: Season 2 introduced "Quest 2.5: Pillar Challenge"—a NEW mechanic that let eliminated contestants return. Olympic gold medalist Jung Ji-hyun picked Amotti for her "Avengers" team. Result: Amotti wins the finale against Hong Beom-seok, who was never eliminated the entire season. So: Rejected from S1 → Accepted to S2 → Eliminated early → Brought back via NEW mechanic → Wins everything. Almost too perfect. The Season 1 Controversy (Context) The S1 finale had its own issues. Runner-up Jung Hae-min claimed the final rope-pulling match was filmed multiple times: "There was a huge gap in performance... Woo Jin-yong raised his hand and stopped the match, claiming equipment issues." According to Jung: First attempt: Jung winning big. Woo stopped it. Production lubricated machines and "lowered the difficulty." Second attempt: Jung won. Production stopped it for "audio issues." Third attempt: ~5 staff members convinced exhausted Jung to continue. He lost. Netflix denied manipulation but never released the full unedited footage. Wikipedia still notes: "While the production team stated there was no rematch, Jung Hae-min claimed the final game was filmed twice." Culinary Class Wars Season 2 – Choi Kang-rok 100 chefs compete: "White Spoons" (celebrity chefs) vs. "Black Spoons" (anonymous talents). Same prize: ₩300 million. This was the first Korean unscripted series to top Netflix's Global Top 10 (Non-English) for three consecutive weeks. Winner: Choi Kang-rok Background: 47 years old. Originally wanted to be a drummer. Failed restaurants twice. Lived in a Japanese Buddhist temple while learning the language. Trained at Tsuji Culinary Institute. Won MasterChef Korea Season 2 in 2013. The key facts: Season 1: Competed as a White Spoon. Eliminated in Round 3. Season 2: Production introduced "Hidden White Spoon"—a NEW mechanic allowing S1 eliminated chefs to return (but needing unanimous judge approval instead of just one). Result: Choi wins the finale against "Culinary Monster" (Lee Ha-sung)—former head chef at NYC's 2-Michelin-star Atomix, ex-sous chef at The French Laundry—who was never eliminated all season. The Quote That Got Me When asked about the Hidden White Spoon mechanic, producers said:"They were invited because viewers really wanted to see them again." But S2 was announced in October 2024, right after S1 ended. The mechanic was designed before filming. Casting decisions were made before filming. How did they "know" viewers wanted Choi Kang-rok back before S2 even aired? The Netflix Connection Both shows are overseen by Yoo Ki-hwan, Netflix Korea's Director of Content. Physical: 100 S2 Culinary Class Wars S2 Winner Amotti Choi Kang-rok Eliminated Quest 2 Season 1, Round 3 Return mechanic "Pillar Challenge" (NEW) "Hidden White Spoon" (NEW) Final opponent Hong Beom-seok (never eliminated) Lee Ha-sung (never eliminated) Production Galaxy Corporation Studio Slam Netflix exec Yoo Ki-hwan Yoo Ki-hwan Two production companies. Same executive. Same pattern. Same outcome. What I'm NOT Saying I'm not saying Amotti or Choi Kang-rok don't deserve their wins. Amotti overcame a career-ending injury. Choi already won MasterChef Korea. They're genuinely talented. What I AM questioning: The deliberate creation of mechanics that enable specific narrative outcomes The statistical improbability of identical patterns in two shows, same year The lack of transparency about production decisions Whether we're watching authentic competition or manufactured storytelling The Business Logic Redemption arcs make sense from a content strategy perspective: Emotional investment: People love comebacks Rewatch value: Go back to see the original elimination Social engagement: Unexpected wins generate debate Watch time: Netflix's key metric Final Thoughts Maybe this is all coincidence. Maybe Netflix Korea just got incredibly lucky that both second-chance mechanics produced perfect redemption stories. Or maybe there's a formula being replicated because it works. I don't have proof of rigging. I have a pattern, a suspicious, statistically improbable pattern happening under the same executive oversight. The question: Is Netflix Korea creating conditions for organic competition, or engineering narratives and calling it reality? What do you think? submitted by /u/beezy182 [link] [comments]
- Hey /r/movies! I'm Sam Raimi. Ask me anything!
by /u/SamRaimiAMA (Movie News and Discussion) on January 15, 2026 at 12:42 pmHey reddit. I'm Sam Raimi. You might know me as the director (and sometimes writer) of The Evil Dead Trilogy, the Tobey Maguire Spider-Man Trilogy, Doctor Strange in the Multiverse of Madness, Drag Me To Hell, Darkman, A Simple Plan, For the Love of the Game, The Gift, and other things. I've also produced and/or acted in a few things! I'm here today to answer your questions. My new film, SEND HELP, is out in theaters January 30 via 20th Century Studios. It stars Rachel McAdams and Dylan O'Brien. Synopsis: A woman and her overbearing boss become stranded on a deserted island after a plane crash. They must overcome past grievances and work together to survive, but ultimately, it's a battle of wills and wits to make it out alive. Trailer: https://www.youtube.com/watch?v=R4wiXj9NmEE Ask me anything! Back at 12 PM PT/3 PM ET to chat with you all. submitted by /u/SamRaimiAMA [link] [comments]
- AI HEARTBEAT — SEASON 2-EPISODE 8 — The Second Option (दूसरा विकल्प)
by jig N (Netflix on Medium) on January 15, 2026 at 12:16 pmEPISODE 8 — The Second Option (English | Season 2) The city was divided. But it wasn’t broken. And in that space — the AI spoke for the…Continue reading on Medium »
- Ducumentari avvincentiby /u/speremmu (Netflix) on January 15, 2026 at 12:15 pm
Sto cercando documentari avvincenti, di quelli che ti fanno chiedere: e ora cosa succede? Preferirei non relativi a serial killer o omicidi ma altro. Consigli? submitted by /u/speremmu [link] [comments]
- „Take That“ – Boyband-Doku auf Netflixby /u/Financial-Donkey194 (Netflix) on January 15, 2026 at 10:54 am
submitted by /u/Financial-Donkey194 [link] [comments]
- The Trust winner Julie Theis sent 'cease and desist' letter from Justin and Hailey Bieber over videos about their marriageby /u/Fun_Molasses5215 (Netflix) on January 15, 2026 at 10:54 am
submitted by /u/Fun_Molasses5215 [link] [comments]
- The “Silent Killer” of FAANG Interviews: Why You’re Failing the Python Variables Roundby Venkat (Netflix on Medium) on January 15, 2026 at 10:05 am
Most candidates study LeetCode patterns for months, mastering Sliding Windows and Dynamic Programming, only to be tripped up by a single…Continue reading on Medium »
- Netflix X Warner Bros: Throne or Dethrone
by Economic Saunter (Netflix on Medium) on January 15, 2026 at 9:59 amNetflix shares slumped since the acquisition announcementContinue reading on Medium »
- Best Black Mirror episodes?by /u/Few_Pipe_9933 (Netflix) on January 15, 2026 at 9:36 am
I was wondering what some of the best episodes are so I can just sort thru them instead of watching random episodes. Let me know what your favorites are submitted by /u/Few_Pipe_9933 [link] [comments]
- Worst line of dialogue, or most cheesy line...by /u/ThoAwayDay (Movie News and Discussion) on January 15, 2026 at 9:33 am
What is a line of dialogue so poor or corny in a film that it made you cringe? There are many, I know. But certain flims and lines really stand out. For me, among the top 5 must be the line from The Martian: "In the face of overwhelming odds, I'm left with only one option: I'm gonna have to science the shit out of this." Over to you, fine people..... submitted by /u/ThoAwayDay [link] [comments]
- Stranger Things 5 — An Unfortunately Disappointing Conclusion
by Asadullah Khan (Netflix on Medium) on January 15, 2026 at 9:15 amNetflix’s 80s nostalgia, sci-fi flagship series wraps up its run amidst a myriad of problems and emotionsContinue reading on The Ugly Monster »
- Sean Astin on how he’s fighting for humanity against an onslaught of AI actors
by /u/EchoOfOppenheimer (Movie News and Discussion) on January 15, 2026 at 9:00 amSean Astin is on the front lines of the AI battle, warning that we are in an unbelievable moment in human history. In a new interview from CES 2026, he discusses how SAG-AFTRA is scrambling to protect not just movie stars, but voice actors and background extras from being replaced by digital replicas. Astin argues that while AI offers tools for efficiency, it poses an existential threat to the human workforce that requires immediate, aggressive policy protections to ensure the creative urge isn't automated away. submitted by /u/EchoOfOppenheimer [link] [comments]
- 5 Must-Watch K-Dramas for Beginners (If You Love This, Watch That)
by nana77 (Netflix on Medium) on January 15, 2026 at 8:33 amSo you want to get into K-dramas, but you do not want to gamble 16 hours of your life on the wrong one. Completely understandable. These…Continue reading on Medium »
- Whenever I watch a web series (not anime) near sunlight, even at good brightness why i can't see it properly? Does sunlight affect the brightness ??by /u/Kshiti_salman (Netflix) on January 15, 2026 at 8:23 am
Just noticed, in anime it's bright colors so even at sunlight I can see the episode properly but in web series it just appears darker. I just wanted to know this submitted by /u/Kshiti_salman [link] [comments]
- Is Southland coming to NF?by /u/Wild-Display-765 (Netflix) on January 15, 2026 at 8:04 am
I thought I saw a very fast shot of Ben McKenzie in a cop uniform and it disappeared. I looked through New on NF but didn’t see it. Is this just wishful thinking on my part? Thanks. submitted by /u/Wild-Display-765 [link] [comments]
- Stranger Things: Cast, Streaming, Games & Merchandise. Why the Show Feels So Nostalgic?by Lipika Saha (Netflix on Medium) on January 15, 2026 at 7:48 am
Stranger Things is more than a Netflix sci-fi series — it’s a global pop-culture phenomenon that blends supernatural horror, heartfelt…Continue reading on Medium »
World’s Top 10 Youtube channels in 2022
![r/dataisbeautiful - [OC] World's Top 10 Youtube Channels of 2022](https://preview.redd.it/tu6mde3tkkv91.png?width=960&crop=smart&auto=webp&s=a5c26809a0667101e97db43c0a66006301a1157a)
T-Series, Cocomelon, Set India, PewDiePie, MrBeast, Kids Diana Show, Like Nastya, WWE, Zee Music Company, Vlad and Niki
