criteo-research/CausE
Code for the Recsys 2018 paper entitled Causal Embeddings for Recommandation.
Implements causal embedding methods (CausE-avg, CausE-prod-T, CausE-prod-C) that learn user and product representations by modeling counterfactual responses under uniform exposure, addressing selection bias in recommendation systems. Built with TensorFlow, it provides configurable training via command-line flags for embedding dimension, counter-factual regularization weighting, and early stopping, supporting MovieLens and Netflix datasets.
248 stars. No commits in the last 6 months.
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Language
Python
License
Apache-2.0
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Last pushed
Oct 24, 2018
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