/Vladislav Vorotilov

Scaling The Instagram Explore Recommendations System tl;dr: Instagram has introduced a multi-stage approach to ranking, including retrieval, first-stage ranking, second-stage ranking, and final re-ranking. The system leverages caching and pre-computation with a Two Towers neural network, making it more flexible and scalable. Techniques like Two Tower retrieval, user interactions history, and parameters tuning - including Bayesian optimization and offline tuning - are employed. The article emphasizes the clever use of caching and pre-computation allowing for heavier models in ranking stages, and concludes with a note on the ongoing complexity and future improvements.

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