15 lines
979 B
Markdown
15 lines
979 B
Markdown
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# Notification Light 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 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.
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## Directory Structure
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- BUILD: this file defines python library dependencies
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- model_pools_mlp.py: this file defines tensorflow model architecture for the notification light ranker model
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- 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
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- eval_model.py: the main python entry file to set up the overall model evaluation pipeline
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