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Experiments on item embedding by treating them like sequence of items that a user interact with.

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trantrikien239/sequence-item-embedding

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Entrypoints:

  1. problemframing-eda-featureengineering.ipynb: EDA, problem definition, and feature engineering
  2. baseline_models.ipynb: Training and tuning of baseline models (feature-based models)
  3. rnn_models.ipynb: Training of sequence-based models

Modules: rnn_dataset.py, rnn_model.py, rnn_trainer.py, utils.py

If you want to re-create the results, please create a new environment with requirements.txt and put the data files into the ./data/instacart-market-basket-analysis/ folder. The Instacart dataset can be found here: https://www.kaggle.com/competitions/instacart-market-basket-analysis/data

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Experiments on item embedding by treating them like sequence of items that a user interact with.

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