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twitter-team b389c3d302 Open-sourcing pushservice 
Pushservice is the main recommendation service we use to surface recommendations to our users via notifications. It fetches candidates from various sources, ranks them in order of relevance, and applies filters to determine the best one to send.
 		2023-05-19 16:27:07 -05:00
..
BUILD Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
README.md Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
__init__.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
deep_norm.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
eval_model.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
model_pools_mlp.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00

README.md

Notification Light Ranker Model

Model Context

There are 4 major components of Twitter notifications recommendation system: 1) candidate generation 2) light ranking 3) heavy ranking & 4) quality control. This notification light ranker model bridges candidate generation and heavy ranking by pre-selecting highly-relevant candidates from the initial huge candidate pool. It’s a light-weight model to reduce system cost during heavy ranking without hurting user experience.

Directory Structure

  • BUILD: this file defines python library dependencies
  • model_pools_mlp.py: this file defines tensorflow model architecture for the notification light ranker model
  • deep_norm.py: this file contains 1) how to build the tensorflow graph with specified model architecture, loss function and training configuration. 2) how to set up the overall model training & evaluation pipeline
  • eval_model.py: the main python entry file to set up the overall model evaluation pipeline