End-to-end CTR prediction for push notifications
Click-through model, owned end to end, that decides which notification to send and when. +153% engagement.
A click-through-rate prediction model for push notifications, owned from research through production.
For every notification slot, the system needs to decide which one to send and at what moment to send it, to maximize the chance the user actually taps. Owning the model end to end meant taking the project from problem definition to live production traffic.
The full project lifecycle: data preparation, feature engineering, modeling, evaluation, training pipeline, online A/B testing, and production serving. Combined LightGBM and TensorFlow models depending on signal type. Lifted click-through rate by +153% over baseline.
Sole author. Owned the full project lifecycle from research to production.
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