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