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How we built personal recommendations in the world’s most significant classified

The talk was accepted to the conference program


Context: setting the task - a feed of personal recommendations on the main page. How to launch recommendations in production when you have 150 million items and 100 million users? I will share my experience, tell you about the pitfalls
A quick overview of the arsenal of models: classic ML approach
A quick overview of metrics starts with product metrics.
The basis of everything: fast experiments and analytics on actual data
Where to start? Classical matrix factorization and its launch pattern.
What problems did you encounter at this stage
Little more advanced: switching real-time user features and history. An alternative approach with simpler models.
Advanced models: Let's add neural networks, the strength is in diversity.
Mixing models - great blender
How does it work in production? Replaced Go with Python, what happened to time to market?
And again, about the experiment cycle, I'll tell you about product metrics.