torecsys
ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop an ecosystem to experiment, share, reproduce, and deploy in real-world in a smooth and easy way.
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- pytorch-logo-flame.ico
- pytorch-logo-flame.svg
- conf.py
- index.rst
- modules.rst
- torecsys.data.dataloader.fields.rst
- torecsys.data.dataloader.rst
- torecsys.data.dataset.rst
- torecsys.data.negsampling.rst
- torecsys.data.rst
- torecsys.data.sampledata.rst
- torecsys.data.subsampling.rst
- torecsys.estimators.ctr.rst
- torecsys.estimators.emb.rst
- torecsys.estimators.ltr.rst
- torecsys.estimators.rst
- torecsys.functional.rst
- torecsys.inputs.base.rst
- torecsys.inputs.rst
- torecsys.layers.ctr.rst
- torecsys.layers.emb.rst
- torecsys.layers.ltr.rst
- torecsys.layers.rst
- torecsys.losses.ctr.rst
- torecsys.losses.emb.rst
- torecsys.losses.ltr.rst
- torecsys.losses.rst
- torecsys.metrics.rst
- torecsys.models.ctr.rst
- torecsys.models.emb.rst
- torecsys.models.ltr.rst
- torecsys.models.rst
- torecsys.rst
- torecsys.utils.decorator.rst
- torecsys.utils.logging.rst
- torecsys.utils.rst
- torecsys.utils.training.rst
- torecsys.utils.utils.rst
- make.bat
- Makefile
- requirements.txt
- Example-0-Training-a-Matrix-Factorization-in-torecsys.ipynb
- Example-1-Training-a-Bayesian-Persoanlized-Ranking-Matrix-Factorization-in-torecsys.ipynb
- Example-2-Trainer-of-Field-aware-Factorization-Machine.ipynb
- (dev) ctr_classification.py
- (dev) ctr_ranking.py
- (dev) listwise_ranking.py
- (dev) starspace_embedding.py
- README.md
- __init__.py
- test_dataloader.py
- test_dataset.py
- test_estimators.py
- test_inputs.py
- test_layers.py
- test_losses.py
- test_metrics.py
- test_miners.py
- test_models.py
- test_sampledata.py
- test_sampling.py
- test_subsampling.py
- test_trainer.py
- __init__.py
- __init__.py
- index_field.py
- sentence_field.py
- __init__.py
- collate_fn.py
- __init__.py
- dataset.py
- __init__.py
- download_data.py
- load_data.py
- __init__.py
- sub_sampling.py
- __init__.py
- concat_inputs.py
- image_inp.py
- list_indices_emb.py
- multi_indices_emb.py
- multi_indices_field_aware_emb.py
- pretrained_image_inp.py
- sequence_indices_emb.py
- single_index_emb.py
- stacked_inp.py
- value_inp.py
- __init__.py
- inputs.py
- __init__.py
- attentional_factorization_machine.py
- bias_encoding.py
- bilinear.py
- bilinear_interaction.py
- compose_excitation_network.py
- compress_interaction_network.py
- cross_network.py
- dynamic_routing.py
- factorization_machine.py
- field_aware_factorization_machine.py
- inner_product_network.py
- mixture_of_experts.py
- multilayer_perceptron.py
- outer_product_network.py
- position_embedding.py
- positon_bias_aware_learning_framework.py
- wide.py
- __init__.py
- generalized_matrix_factorization.py
- starspace.py
- __init__.py
- __init__.py
- regularization.py
- __init__.py
- functional.py
- skipgram.py
- __init__.py
- functional.py
- groupwise_ranking_loss.py
- pairwise_ranking_loss.py
- pointwise_ranking_loss.py
- __init__.py
- __init__.py
- accuracy.py
- functional.py
- novelty.py
- __init__.py
- uniform_batch_miner.py
- __init__.py
- attentional_factorization_machine.py
- deep_and_cross_network.py
- deep_ffm.py
- deep_fm.py
- deep_mcp.py
- deep_moe.py
- deep_session_interest_network.py
- elaborated_entire_space_supervised_multi_task.py
- entire_space_multi_task.py
- factorization_machine.py
- factorization_machine_supported_neural_network.py
- fat_deep_ffm.py
- feature_importance_and_bilinear_feature_interaction_network.py
- field_aware_factorization_machine.py
- logistic_regression.py
- multigate_moe.py
- neural_collaborative_filtering.py
- neural_factorization_machine.py
- position_bias_aware_learning_framework.py
- product_neural_network.py
- wide_and_deep.py
- xdeep_fm.py
- __init__.py
- matrix_factorization.py
- starspace.py
- __init__.py
- learning_to_rank_wrapper.py
- personalized_reranking.py
- __init__.py
- sequential.py
- __init__.py
- model_validation.py
- __init__.py
- torecsys_pipeline.py
- torecsys_trainer.py
- __init__.py
- decorator.py
- logging.py
- operations.py
- typing_extensions.py
- __init__.py
- .gitignore
- .readthedocs.yaml
- LICENSE
- MANIFEST.in
- pyproject.toml
- README.md
- requirements.txt
- setup.cfg
- setup.py
- TODO.md
# Installation Guide
1. Get the code
git clone https://github.com/p768lwy3/torecsys
Downloads the entire project code from GitHub to your computer.
cd torecsys
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install -r requirements.txt
Installs the Python libraries listed in requirements.txt (or similar).
jupyter notebook
Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.
If it runs without errors and prints output in the terminal, it worked.
// repository documentation
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