Based on this YouTube video's total view count of 69.5K views and industry-standard rates, the estimated total earning is $49 - $139 through ad revenue. Historical data is not yet available to calculate daily, weekly, or monthly averages.

Explore yeah... Netflix only wants you to watch THEIR movies with 69,544 views, 1,728 likes, and 232 comments. Experience the impact of this video content that has captured audience attention.

Try Hark Pro: https://hark.com/?utm_source=influencer&utm_medium=social&utm_campaign=slidebean Hark Pro is an operating system for getting things done. It remembers the context of your life and uses a computer in the cloud to do things for you across the web. Anything you need to do, Hark can handle it. Available on web, iOS, and Android. -- Check out our own movie recommendation tool to help you find your next great watch. Try it for free 👉 https://movies.slidebean.com/ -- We pay for Netflix, HBO, and a bunch of other streaming services. They know what we watch, what we like, and even when we give up halfway through a movie. So why are their recommendations still so bad? Caya got tired of spending more time scrolling than actually watching movies. So he compared Netflix recommendations across our team, dug into how streaming algorithms work, and discovered that what shows up on your homepage isn't necessarily what you're most likely to enjoy. Even the thumbnails are personalized to get you to click. But if Netflix can't reliably recommend a good movie, who can? In this episode, we break down how IMDb, Letterboxd, and Rotten Tomatoes actually rate movies, why audience scores can be misleading, and whether critics are any better at predicting what we'll enjoy. Then Caya tries building his own movie recommendation system, experimenting with audience ratings, movie characteristics, and critics whose taste matches his. Turns out, recommending a great movie is a lot harder than giving it five stars. And maybe the problem is that taste is just too personal for any algorithm to get completely right. -- 👕 Grab our new Slidebean tees! 👉 https://shop.slidebean.com/ -- Recommended video: https://youtu.be/t_GK06_3UY8?si=m3SMtdYFwl0HRPdU -- Follow us Slidebean LinkedIn: http://linkedin.com/company/slidebean Instagram: http://instagram.com/slidebean Caya Letterboxd: https://letterboxd.com/cayahere/ Instagram: https://www.instagram.com/caya.here/ X (Twitter): http://twitter.com/cayahere LinkedIn: https://www.linkedin.com/in/caya/ -- 📚 Sources & References Netflix — How Its Recommendation System Works https://help.netflix.com/en/node/100639 Netflix Technology Blog — Artwork Personalization https://netflixtechblog.com/artwork-personalization-c589f074ad76 Netflix Technology Blog — GenRec and AI Recommendations https://netflixtechblog.com/genrec-towards-llm-native-recommendation-at-netflix-f20be6f643e3 Rotten Tomatoes — Audience Score Update https://editorial.rottentomatoes.com/article/audience-score-update/ IMDb https://www.imdb.com/ Letterboxd https://letterboxd.com/ Rotten Tomatoes https://www.rottentomatoes.com/ The Movie Database (TMDB) https://www.themoviedb.org/ -- #Netflix #Streaming #MovieRecommendations #Letterboxd #IMDb #RottenTomatoes #Movies #StreamingServices #Algorithms #AI #MovieRatings #FilmCriticism #Slidebean #HarkPartner

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