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✝️ 𝓟𝓐𝓡𝓛𝓔 ~𝓡𝓕 🔫
✝️ 𝓟𝓐𝓡𝓛𝓔 ~𝓡𝓕 🔫
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Monday 28 September 2026 19:26:28 GMT
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➡️ Machine Learning from Scratch, part 15: a recommender, the kind of algorithm that picks what your feed shows you. Our app is simulated: 300 people and 60 videos. Every person has a hidden taste, every video has a hidden style, and each person has watched only about 12 of the 60 videos. All the algorithm ever sees is who liked which of the videos they watched. How it learns: every person gets two numbers, and every video gets two numbers. Think of them as arrows on a map. For every video a person has seen, the two arrows turn into a chance of a like with the sigmoid from part 2. The algorithm compares that chance with what really happened and nudges both arrows a little. That is gradient descent from part 8, and the whole method is called matrix factorisation. The results, all measured on the videos nobody showed it: After 25 rounds it guesses 85.0% of the hidden likes right. After 3000 rounds it gets 87.8% right. The best any method could do here is 91.1%, because some likes are just random. For every person it recommends the three unseen videos with the strongest match, and 98% of them are videos the person really likes. A random unseen video gets a like 51% of the time. The first person on the list watched only 5 videos. From those five it guessed the other 55 and got 51 of them right. We ran the same program on 20 different simulated apps, and it always landed between 84 and 88%. Real apps use hundreds of numbers for every person and every video, plus signals like watch time and skips. The core idea stays the same: learn numbers for people and for videos, and match them. #machinelearning #ai #recommendersystem #python #coding
➡️ Machine Learning from Scratch, part 15: a recommender, the kind of algorithm that picks what your feed shows you. Our app is simulated: 300 people and 60 videos. Every person has a hidden taste, every video has a hidden style, and each person has watched only about 12 of the 60 videos. All the algorithm ever sees is who liked which of the videos they watched. How it learns: every person gets two numbers, and every video gets two numbers. Think of them as arrows on a map. For every video a person has seen, the two arrows turn into a chance of a like with the sigmoid from part 2. The algorithm compares that chance with what really happened and nudges both arrows a little. That is gradient descent from part 8, and the whole method is called matrix factorisation. The results, all measured on the videos nobody showed it: After 25 rounds it guesses 85.0% of the hidden likes right. After 3000 rounds it gets 87.8% right. The best any method could do here is 91.1%, because some likes are just random. For every person it recommends the three unseen videos with the strongest match, and 98% of them are videos the person really likes. A random unseen video gets a like 51% of the time. The first person on the list watched only 5 videos. From those five it guessed the other 55 and got 51 of them right. We ran the same program on 20 different simulated apps, and it always landed between 84 and 88%. Real apps use hundreds of numbers for every person and every video, plus signals like watch time and skips. The core idea stays the same: learn numbers for people and for videos, and match them. #machinelearning #ai #recommendersystem #python #coding
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