😳 Зашёл посмотреть один пост — очнулся в 3 ночи. Как ML заставляет нас залипать в ленте? За каждым «ну ещё одно видео и спать» — рекомендательная система, которая за доли секунды выбирает из сотен тысяч постов те, на которых ты, скорее всего, задержишься. 👤 Денис Амбросов, ML-инженер Райффайзенбанка, разобрал, как устроены рекомендации в крупных сервисах → от первых данных до выдачи в проде. Больше технических деталей и практика — на вебинаре с Денисом🔽 ➕ Как на 5,5 млн просмотров собрать рекомендательную систему с матричной факторизацией и бустингом; ➕ Почему важно не просто угадать интересный контент, но и правильно расположить его в ленте; ➕ Как трансформер предсказывает вероятность 19 действий пользователя — от лайка до жалобы; ➕ Почему случайное разделение данных может научить модель «заглядывать в будущее»; ➕ Как проверить качество рекомендаций и подготовить модель к продакшену. 🔥 СМОТРЕТЬ НА YOUTUBE 🧡 — я залип 🔥 — хочу больше такого контента @aitalenthubnews
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Telegram — AI Talent Hub (ITMO/Napoleon IT)
Read full article at Telegram — AI Talent Hub (ITMO/Napoleon IT)I went to look at one post and woke up at 3 am. How does ML keep us stuck in our feed?
😳 I went to look at one post and woke up at 3 am. How does ML keep us stuck in our feed? Behind every “well, one more video and sleep” is a recommendation system that, in a split second, selects from hundreds of thousands of posts those on which you are most likely to linger. 👤 Denis Ambrosov,...
AI Summary & Key Takeaways
- 😳 I went to look at one post and woke up at 3 am
- How does ML keep us stuck in our feed
- Behind every “well, one more video and sleep” is a recommendation system that, in a split second, selects from hundreds of thousands of posts those on which you are most likely to linger
I went to look at one post and woke up at 3 am. How does ML keep us stuck in our feed?
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