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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
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lib Open-sourcing pushservice 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.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
features.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
graph.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
model_pools.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
params.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
run_args.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00
update_warm_start_checkpoint.py Open-sourcing pushservice 2023-05-19 16:27:07 -05:00

README.md

Notification Heavy 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 heavy ranker model is the core ranking model for the personalised notifications recommendation. It's a multi-task learning model to predict the probabilities that the target users will open and engage with the sent notifications.

Directory Structure

  • BUILD: this file defines python library dependencies
  • deep_norm.py: this file contains how to set up continuous training, model evaluation and model exporting for the notification heavy ranker model
  • eval.py: the main python entry file to set up the overall model evaluation pipeline
  • features.py: this file contains importing feature list and support functions for feature engineering
  • graph.py: this file defines how to build the tensorflow graph with specified model architecture, loss function and training configuration
  • model_pools.py: this file defines the available model types for the heavy ranker
  • params.py: this file defines hyper-parameters used in the notification heavy ranker
  • run_args.py: this file defines command line parameters to run model training & evaluation
  • update_warm_start_checkpoint.py: this file contains the support to modify checkpoints of the given saved heavy ranker model
  • lib/BUILD: this file defines python library dependencies for tensorflow model architecture
  • lib/layers.py: this file defines different type of convolution layers to be used in the heavy ranker model
  • lib/model.py: this file defines the module containing ClemNet, the heavy ranker model type
  • lib/params.py: this file defines parameters used in the heavy ranker model