Problem Statement and Metrics
Explore how to establish clear problem statements and select appropriate offline and online metrics to design scalable video recommendation systems. Understand the balance between exploration and exploitation, and the technical requirements for retraining and low latency inference to maximize user engagement.
Video recommendations
1. Problem statement
Build a video recommendation system that personalizes content for YouTube users. The main goal is to maximize user engagement by recommending videos they are likely to watch and enjoy. But it doesn’t stop there—we also want to introduce new and diverse content, not just more of what users already watch.
Think of it like this: If someone watches cooking videos all day, the system shouldn’t just give them more of the same. It should also suggest travel vlogs, tech reviews, or short films they might find interesting.
Goals:
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Personalized recommendations based on user behavior
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Increase user engagement (watch time, click-throughs, conversions)
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Promote fresh content and not just historical favorites
