Data Science and Recommender Systems with Andrew | 365 Data Use Cases
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Hi everyone and welcome to the next episode of 365 Data Use Cases! In this series of super informative videos, we explore the diverse areas where data is widely used nowadays. Let's be honest, every industry relies on data for insights and strategies. But you'll be amazed how fascinating some of these cases are! This one is no exception!
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In this episode, our friend Andrew from DataLeap will share his favourite data use case – Recommender Systems!
Have you ever thought about the content you see online and why you see it? Well, Andrew has, and he will tell you all about how data models and algorithms recommend content when you do a Google or a YouTube search. He shares that “YouTube has an idea of what videos it can recommend to you based on your behavior and what kind of user you are similar to, based on your demographic and other information you’ve willingly shared". Curious, right? Watch the full video to learn how data science and recommendation systems determine what content is the most relevant to you!
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We will continue the series with plenty more exciting data use cases like this one.
Stay tuned!
0:00 | Intro
0:46 | Why do FAANG companies need recommender systems
1:42 | The YouTube Algorithm
2:26 | The King Regent of Recommendations: Google
2:50 | Recommendation system architecture
3:01 | Stage 1: Candidate generation
3:26 | Stage 2: Scoring
4:38 | Stage 3: Re-ranking
4:56 | Cool tips and resources from Andrew
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