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I'm really embarrassed about this, but these are the last four movies I've watched on streaming. I didn't want to watch them. They were just there, like Netflix, HBO, all these damn services that I pay for all the time. They were just making me watch these movies, and aren't they supposed to be some kind of sophisticated Why did they promote movies that I do n't really like? We now have access to an almost unlimited library of movies, but it feels like the same things are being pushed over and over again. I don't know about you, my friend, but choosing a movie to watch is hard.
Sometimes I stare at this thing for hours and just watch trailers and don't even pick one. And don't these companies know our taste better? Don't they track what we watch, what we like, what we stop watching? And yet they're not very good at recommending things. Is there perhaps a better way to get recommendations? One that isn't tied to the subscriptions we pay, to what Netflix wants us to watch? And social media continues to steal our time. The few films I managed to squeeze in a week became almost a ritual for me. So how do we choose the best one? I looked at all the streaming services, movie ratings, recommendation systems, and found that they all had the same terrible flaw. In the end, I decided to create my own and make it free for
everyone. So let's get to the movie recommendations. First, we need to understand why these recommendations are so terrible. What happens on the backend of these platforms? I want you to look at my Netflix home screen. This is an actual snapshot of every movie series Netflix is showing me right now. "Show" is an important word in this video. We will use it often, but how is this library built? You may not remember, but when you first sign up for Netflix, you are forced to choose three or four movies that you like.
This is a completely superficial approach to recommendations, but that's how they start sorting you into categories. Did you like explosions, historical films, or women's films rather than adventure films? The promise is that the platform will learn from your specific tastes the more material you watch. But that's not entirely true. Netflix is n't really interested in knowing you that well. During an average Netflix scroll, you'll see, say, 150 different pieces of content, which sounds like a lot, but in reality, the entire Netflix catalog in Costa Rica, where I live, is about 4,000 pieces of content. So it would take me about 18 years of continuous viewing to cover this entire library. So Netflix, of course, needs a
way to filter this so that they show me the part that I'm most likely to like. But what are they optimizing for? Is this the taste? How do they know what we like? Remember this thought. TV series are our whole range of problems, which we can talk about in a future video, but in this one we will focus only on movies. Okay, after some resistance from the team about not wanting their guilty pleasures to be on YouTube, this is Vinnie's gallery. This is Jose's movie. This is a Favi film. This is Josh's movie. Have you noticed anything strange? Let me help. These are all movies that are on repeat for two or more of us, regardless of gender,
age, or taste. They are always here. I mean, No Country for Old Men is a great movie, but is it really to Pao's taste? It looks a little out of place, doesn't it? Most of our entire gallery repeats the same movie for all of us. Maybe Netflix is particularly interested in pushing this movie to us right now? According to Netflix's own website, their recommendations are based on a number of factors, including viewing history, movie ratings, and other members with similar tastes.
How do they even know my taste? Movie information such as genre, category, actors, release year. They also remember things like the time of day you watch, the languages you prefer, the device you use, and how long you've been watching other Netflix movies. They supposedly don't use demographic information like age and gender, but that seems a bit unlikely when you compare the feeds of our male and female colleagues. But something more suspicious is happening if you look more closely. Take a look at the thumbnail of the movie " Kung Fu Panda 4". This is how it looks in my feed. This is what she looks like in Vinnie's tape.
It turns out that Netflix's algorithm actually dynamically changes thumbnails based on your viewing history to manipulate you into clicking. So they track your viewing time, your content preferences, to supposedly create this personalized experience. But ultimately, they're just using this data to direct us toward what they want to promote. In an actual Netflix tech blog post, engineers openly stated that the recommendation system takes into account Netflix's business goals, balancing new releases with evergreen titles. In other words, your recommendations are not optimized solely for your perceived taste. And then there's the obvious incentive, you know, why pay to promote someone else's content when Netflix can just promote its own?
I know, this is crazy. No offense. In 2025 alone, they spent over a billion dollars on film licensing in the first half of that year alone. In comparison, they spent less than $0.5 billion on Netflix original films during the same time period. Movies that Netflix can, of course, then place for free on its entire homepage. So, back to my little experiment. I wanted to create something not just for myself, but something that everyone could use. And at the end of the video you'll see that we've created something useful. But I started with the idea of a better recommendation algorithm, right? One that will be liked, will understand possible films for different categories. And my first idea when creating this was to try to somehow connect to the
six streaming services that I already pay for. But doing this was messy because you needed to access them through an API. It depends on the country etc. So, for better or worse, I didn't start with streaming services, nor did I limit myself to them. I started with every movie listed on public movie aggregators, regardless of streaming service. My first idea was to create a system based on user ratings, the wisdom of the crowd, the will of the people. Okay, I would say the best user rating platforms are Letterboxd, IMDb, and Rotten Tomatoes. But here is "The Secret Life of Walter Mitty." It has a 7.3% rating on IMDb. It has a roughly 52% "rotten" rating on Rotten Tomatoes, and a 3.9 on Letterboxd. So
it probably won't be that easy. It turns out that the way movies are ranked on these platforms is actually very different, and, to be honest, a little strange. So you've probably seen the classic, theoretically very simple IMDb rating system from 1 to 10, right? One is nonsense, 10 is absolute cinema, without decimal points. The system only allows whole numbers, so half points are not counted, but you still get decimals when the platform averages all the scores. This system produces lists of the 250 best films, which, frankly, are an interesting test for movie lovers. But how to distinguish a two from a three? In my opinion, both are pretty bad. I mean, anything below five is probably bad. How to distinguish six from seven? I've always found the 10-point
system problematic because, in my opinion, it's too broad a scale for something like liking or disliking a film. For example, my grades in school ranged from one to a hundred. Let's turn that into one to ten, shall we? So, my passing score was seven. Anything below seven meant I didn't pass. So do I really care if it's a two or a four? Sure, there are academic reasons for low scores, there is progress, recovery, but movies aren't students, right? So for me, anything below six is just bad. Well, Letterboxd agrees with my dislike of this scale, because they use a five-star system. For those unfamiliar with Letterboxd, it is the fastest growing social platform
for finding and rating movies. Letterboxd.com I am familiar with Letterboxd. I literally spend 2 hours every day on Letterboxd, planning what to watch that evening. You can follow me and listen to my recommendations. But anyway, Letterboxd uses a five-star scale, but it's actually a scale of 0.5 to five. That's the whole point. They include half stars. So when you have half stars, is it really like a 10-point scale like on IMDb? Although I would say that on a five-point scale, one star versus two is actually much more relevant. For example, I gave " Fountain of Youth"
only one star. I thought this movie was terrible. Uh, but " Backstage 2", which I didn't particularly like, here I can appreciate some of his things. I gave the movie "Backrooms" two stars and, in my opinion, I can tell the difference between one star and two stars. On a 10-point scale, that would be two points against four. And, at least for my brain, they are harder to distinguish. But, as with numbers, people have different attitudes towards stars around the world. For example, in Japanese culture, many places on Google Maps have three stars, and this is considered an excellent rating for a restaurant on Google. In America, a three-star restaurant is like a sign that has run away. Roger Ebert and the Chicago Tribune use a four-star system, while
publications such as The Guardian and Rolling Stone use a five-star system. So it's not very clear. So let's take a look at Rotten Tomatoes. Rotten Tomatoes has two different rating systems: the Tomatometer score, which is calculated based on reviews published by Tomatometer-approved critics, and the Popcorn Meter score, which is calculated based on ratings provided by Rotten Tomatoes users. Okay, so this is what I'm interested in for my user rating experiment. I always thought there was something like a binary system on Rotten Tomatoes. It's like or dislike, thumbs up, thumbs down, something like Netflix. But
it turns out that this is not the case at all. Rotten Tomatoes users give the film a rating of 0.5 to 5 stars, as does Letterboxd. OK. Sounds simple enough. But this is a mess, because these ratings will then be aggregated and converted into an approval percentage. So, if 60% or more people give a movie at least 3 out of 1/2 stars, then that movie will be rated as " hot." But if less than 60% of people give a movie 3 out of 1/2 stars, then that movie will be rated as " outdated." So, the grading system is similar, but the result is displayed in a binary system. The same thing happens with "fresh" and " bad" ratings based on
critic ratings. The scales on these three platforms are more similar than we think, but the user experience is very different when posting or using these ratings. The practical difference is not so much in accuracy as in framing. In a five-star system, this might encourage broader, more positive categorical judgments, whereas this 10-point numerical system might seem more analytical and a bit rigid. Most of the time, I see these estimates and think very little about what goes into them. They are simply shorthand for content filtering. Okay, so let's get back to the experiment. My first idea was to use a combination of these user ratings with all the pros and cons to create a By the way, I'm creating this tool with a big
goal in mind, so that by the time this video is published, you'll have something available. If so, I'll post a link here. If not, then no. So, the first thing we needed to do was create our own little movie database, which we did. So, we have about 11,000 films here. We're going to continue to enrich this data, but we have genres, and we have categories, but the most important thing, at least for this first version, is that we have an IMDb rating, a Rotten Tomatoes rating, and a Metacritic rating for every movie in our little database. By the way, it is built on a movie database, not the IMDb API, because their API is expensive and corporate, while this one is free and community-developed. It is used by Plex and Letterboxd. So, my initial search
turned up about half a million names, which is not what we're going to do. 300,000 titles had some cross-reference data from IMDb that we could use. Maybe we'll use it eventually. But for now, we'll just start with these first 11,000 names. The only reason we can take the time to create things like this is because we can automate a lot of the work, and we use Hark Pro, which also sponsored this section of the video. Hark Pro is an artificial intelligence-based assistant that takes the work off your shoulders. It remembers the context of your life. It uses a computer in the cloud to do things for you online, and it can pull data from tools
you already work with, like Google Docs or Slack. So, it's truly a proactive operating system for your life. A significant portion of all this data enrichment that we had to do was done by Hark. But the thing that saved me the most time was when I asked him to create a panel for me of every set in the video, what we needed to shoot, what was left, everything that was pulled from our script. And he created a panel. He made our live preview, which I can check instead of going through the entire document. And since I'm on the road so much, he does all the road errands too. If I need to make a reservation at a restaurant near the airport, he can find a place for
dinner, or order me an Uber as soon as I arrive, or email the hotel to request a late check-out. And it shows me every email as a draft before sending anything. So my favorite part of this is the panels. These are like little mini- apps that Hark creates that can be used to track just about anything, like a trip countdown that shows the days until your next trip, the time of your flight, or the weather at your destination. And then it switches to the exit you need to go to and notifies you of any delays your flight may have. So if you're juggling a project that has a lot of moving parts, or if you travel a lot, try the Hark Pro via the link in the description.
Thank you to the Hark team for supporting our channel and helping create this tool. And now that we have this database with information about movies, titles, directors, actors, user critics, it will work as a basis for the different recommendation approaches that we're going to try. Okay, so we have this thing. We have the cast, directors, genres, release dates, showtimes. So, this is the first version of the tool. It grouped movies into categories and then suggests the best possible movie based solely on user ratings from all three platforms. For example, here's the action category, which I would say is pretty good. Crime, yes, I think "The Shawshank Redemption" is a kind of crime. Yes, I mean that for each movie there is a
short description, an average user score. As for the rating, we didn't include Rotten Tomatoes because it's a mess, there aren't any But it's an average of the other two movies, and we're still giving you all the data on it. So does this first attempt really beat what Netflix recommends? I have to say that the recommendations it's giving me so far seem less biased than what Netflix shows me on the homepage. I see few or no, and that alone is a triumph in my opinion. Overall, there are some great recommendations for these categories, but there are some issues as many of these recommendations are movies I've already watched or movies I would never
watch. I know about them, but I didn't want to watch them. Is this a byproduct of the crowd effect I was aiming to achieve with the ranking? Did you just say "The English Patient"? You didn't like it No. How could you not love this movie? How about making it suck? I was hoping that user ratings would be a less biased approach, but maybe I need to better understand the psychology behind movie ratings to fine-tune my recommendation tool. When I see that over 300,000 people gave Odyssey an average rating of 8.5 stars, or that over 170,000 people rated Morbius a disaster, I assume those ratings are a good reflection of the crowd, right? A mix of people
, of different backgrounds, different personalities and, ultimately, different tastes. But is it really? Absolutely not. Just like movie ratings, they are only as unbiased as the people who give them. I want to dig a little deeper to understand who the typical reviewer is. The first thing we need to understand is that the people who rate movies are not the average demographic. They are like a specialized self-selected group. They decided to watch the movie, and then among everyone who watched it, they were motivated to open the app and leave a rating. This already filters out a significant portion of the audience. It's an overrepresentation of people who have strong opinions, who are interested in the genre, or who have a deeper obsession with movies. In addition, ratings
are influenced by expectations and prior information. For example, marketing or franchise history, commercial success compared to the previous film, early word of mouth. All of this sets a point of reference. For example, people tend to rate films that have good box office results higher. This was found in a study in the Journal of Marketing, where strong early box office receipts boost later amateur ratings. Existing assessments can also create this binding. MovieLens conducted an experiment and found that people shifted their ratings towards the rating the system displayed, even if the rating was not real. This was just part of the experiment. Fans are
more likely to watch a franchise they love. Hype will change their expectations. Existing estimates affect later estimates. People rate differently depending on what they watched immediately before. At the same time, audiences respond more positively to actors with whom they identify or who they understand culturally. Additionally, this large IMDb study showed that about 80% of ratings come from men. So what appears here to be a neutral audience assessment actually reflects a stereotypical viewer. This is a viewer who cares enough to evaluate a film, and whose opinion depends on all these external factors. It's kind of a perfect storm for things like review bombardment. Essentially, it's just a group of angry reviewers getting together,
they coordinate an attack of reviews. Just so everyone can downgrade the movie for some reason. We saw this with "The Little Mermaid," the game version. We saw this with Snow White. They were destroyed by audiences before the films were even released, and both films had low box office returns, especially Snow White. And don't get me wrong, as if both films weren't great. They were a money grab. But the point is that the decision was made before the film was released in theaters. And all of this is because of the power these user ratings now have in our society. Snow White" has officially been voted the worst film of all time.
Let's talk about the new Disney live-action film "Snow White And these ratings can be politically motivated, that is, have nothing to do with the quality of the content. This is just a bit of trolling. Take, for example, "Ozymandias, " which was once the highest- rated episode on television, but they got embroiled in a review war with "Game of Thrones." So, knowing that it was a mess, user ratings might not be a very good guide, I wanted to try a different path. One that bypasses this public bias, one that is more tied to the content itself. So, can I create a movie recommendation system based on the content itself? So, my family and I started playing this game a few years ago called "Movie Chain." The concept is very simple. You
take the last movie you watched and then choose a common denominator to move on to the next movie you want to watch. For example, to be honest, we watched Fall Guy because Netflix recommended it. We didn't really like it, but we used Ryan Gosling as a bridge to "Bridge of Spies." And then from "Bridge of Spies" we moved on to "The Martian," because they share a screenwriter, the author of the book. And then from The Martian we turned to Prometheus, because Ridley Scott is the co- director. Uh, that got us into "A," and then Michael Fassbender got us into "Inglourious Basterds." Or you can go either way, right? You can choose a director, screenwriter, music composer. It's a bit like a
numbers game about Kevin Bacon. But it gives you a short list of movies to choose from, so it reduces that decision paralysis due to an excessive number of options. And that has some guarantee or some testable confirmation that the next movie you watch has at least something that you already liked. Now that I've researched this video, I realize that Reddit should have created its own version with its own version of this game in the r/movies subreddit, but I swear my family and I invented our own little version first. Now the question is, can I turn this thing into a recommendation tool? So, here's how it should work. The idea is that you enter the last movie you watched, or any movie you want to use as a base. Let's do, uh, "The Glory of Mary Project,
maybe." Uh, if we're in the mood for it, a little science fiction. Good. This is an obvious coincidence, because we have the same author of the book, and I don't know if it's the same screenwriter. Same genre, so fair enough. I think this is probably the most consistent. Uh, "The Matrix" shares a common era, science fiction, and duration. Steeply. I was expecting to see some Ryan Gosling here, so we'll make some adjustments, but that's the idea. So, a few weeks have passed. I'm not very convinced. The problem is that when the dynamics are processed outside the tool, you choose which link to use to select the next view. So I'm kind of self-referential. This is a method that depends heavily on my
personal taste, but I see two big problems with this tool. The first problem is the similarity, right? I'm not always in the mood to watch the same group of actors star in a similar movie directed by the same guy. And at some point it seemed to me that I had watched the same movie five times in a row. Yes? So in that sense, I have to give Netflix credit. They have a certain randomness to what they recommend. Sometimes they surprise me with very different things. But problem B was actually more difficult. Problem B is that even with significant overlap between films, there is no single predictor of
film quality. For example, " American Hustle" and the film "Amsterdam" have the same director, the same cast, and the same genre. The same goes for Sweeney Todd and Dark Shadows. Both are Tim Burton films, both have Johnny Depp, Helena Bonham Carter, and both are gothic black horror comedies. One is wonderful, the other is nonsense. I could add user ratings to address this issue in the tool, but I think we've already seen that this is related to other issues. So, common denominators are not necessarily the best quality or the predictors of taste. Many actors, screenwriters, and producers have incredible and terrifying films in their portfolio.
Part of the profession. So I was working on how to solve this problem, and then my wife asked me, "What about the film awards?" I mean, the golden statues, the leafy things, they can be good predictors of the movie. After all, I mean, I'm still one of those idiots who gets hyped up around awards season. I watch every film to have my own opinion, to agree with whoever won the award. So I considered this alternative, but I was reminded that rewards are problematic. The once-imaginary glory of winning an Oscar or Golden Globe means less and less with each passing year. There was a lot of controversy, a negative reputational backlash that damaged cultural relevance, perhaps irreparably. For the first time since 2012,
no British actor has been nominated for Best Actor or Best Actress. And the Oscar goes to. A draw. I'm not kidding. for best costume design-this is the hashtag that sparked a movement… As pressure to recognize and include more diverse nominees grew, viewership of episodes of these events declined. People don't show up to these televised events like they used to. They'll start streaming it on YouTube now, in 2020, not just in the hopes that it will reach a wider audience that doesn't pay for cable. Well, the death of a film award might be a whole different video. Let me know if you want us to touch on this topic. But the point is that awards are now less related to quality.
They're more of a desperate attempt to, you know, bring back an industry based on, I don't know, the fame of people and guests, but either way, I don't think it's a great way to find movie recommendations. So I started to lose hope in my recommendation system until I made this old breakthrough. So is there a way to use critic ratings to get better recommendations? I know that throughout all of these experiments, my goal was to avoid these biased recommendations, whether from angry users, streaming executives, or award-giving organizations. And in this attempt at film criticism, it's the exact opposite of what I need.
They are snobs. They have their own opinion. Their job is to criticize art, not create it. But, could it be that these film experts know something that I don't? Could they have the key to unlock it? I wanted to check this out. The next thing we thought about was: could we find a group of respected film critics and then find seven very controversial films that some critics loved and others hated? Now, if we were to rate these same films based on our personal tastes and figure out which critics best fit our taste using these highly controversial films, would we then be able to offer something worth watching? And my theory is that perhaps the best
recommendation is not some complex algorithm at all. Maybe it's just people, you know, finding critics whose taste is closest to mine. I mean, I already follow entire YouTube channels built around people telling me what movies are cool to watch. So as I was making the list, I realized there was something I never really understood, which was how these guys worked. Because, you see, four stars from IndieWire or five stars from Time Out. They put them on trailers and on posters, and I always thought they represented the publication, but that's not really the case. For example, publications such as The Atlantic and The Guardian have many full-time critics, freelancers, and authors, and they all review films independently. So when you see three
stars on The Guardian, it doesn't mean the Guardian gave it three. This could mean that Peter Bradshaw, one of their long-time critics, gave the film three stars. So really, these promotional quotes should be read as if someone at the Guardian gave the film three stars. A few critics have become famous enough to build their own audience around their name or even be quoted by name rather than publication, but they are the exception. Okay, so let's get back to the list. So far, I've narrowed down a list to about 20 of the best film critics I could find who actually rate films by the numbers. Some of these guys only rate movies based on prose or video reviews, and we can't really use them
because we need numbers. I know it's a little unfair, but it is what it is. So, I dug through the archives, and there are about nine films that these guys couldn't agree on. Because I Was Afraid, Blonde, Don't Look Up, The Fountain, The House That Jack Built, Mother of Megapolis, Neon Demon and Only God Forgives. Some of these movies, you know, have a rating of four out of five, and the next one has a rating of three out of five or two out of five. So they can't agree on what a good movie is, but that's great for us because if we go, and that's the idea for this latest version that we're making, if you rate these movies yourself, it's going to be very easy to find a critic that fits your particular taste. Well, we went and did it. Some of
these movies I hadn't watched, so I went and watched them, I did, and I was really impressed. Having a trusted critic is like having a friend who will confirm you even if you're completely wrong about something, but, joking aside, even critics you have great chemistry with don't always agree with you. This is normal. In fact, reading reviews is much more useful for understanding and evaluating a film than just some numerical score. Justification for this number. Now, faced with a different perspective, this is the most useful of this third test, because that's what these streaming video recommendations are all about. After all these experiments, I don't think the problem is that
they don't recommend better things. I think they really recommend it. The problem is that it is very difficult to define your own taste. They try to balance things that seem familiar enough to make you click with things that are different enough to really surprise you. And we're constantly looking for some kind of consensus about whether a film is objectively good, but there's no such consensus. There are movies that impress you and movies that don't. Sometimes you can explain why, sometimes you can't. And perhaps that's the point. Great movies shouldn't force everyone to agree. They should give us something to argue about, something to
think about, something to carry with us after we leave the movie theater. And now, as promised, we are making this tool we created absolutely free for you. You can use this QR code or just the link in the description, and I'd love to hear what movies you recommend. After each video, we have a live broadcast called "Loose Ends," so stay tuned. This usually happens a couple of days after the video is released. Thank you very much for watching. See you next time.
