{
    "info": {
        "author": "Jasper Li",
        "author_email": "jasper.li.wy@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 1 - Planning",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
        ],
        "description": "# ToR[e]cSys\n\n--------------------------------------------------------------------------------\n\nToR[e]cSys is a Python package which implementing famous recommendation system \\\nalgorithm in PyTorch, including Click-through-rate prediction, Learning-to-ranking, \\\nand Items Embedding.\n\n- [Installation](#installation)\n- [Implemented Models](#implemented-models)\n- [Documentation](#documentation)\n- [More About ToR[e]cSys](#more-about-torecsys)\n- [Getting Started](#getting-started)\n- [Examples](#examples)\n- [Authors](#authors)\n- [License](#license)\n\n## Installation\n\n### By pip package\n\n```bash\npip install torecsys\n```\n\n### From source\n\n```bash\ngit clone https://github.com/p768lwy3/torecsys.git\npython setup.py build\npython setup.py install\n```\n\n### Build Documentation\n\n```bash\ngit clone https://github.com/p768lwy3/torecsys.git\ncd ./torecsys/doc\n./make html\n```\n\n## Documentation\n\nThe complete documentation for ToR[e]cSys is avaiable via [ReadTheDocs website](https://torecsys.readthedocs.io/en/latest/).  \nThank you for ReadTheDocs!!!\n\n## Implemented Models\n\n| Model Name | Research Paper | Type |\n| ---------- | -------------- | ---- |\n| [Attentional Factorization Machine](torecsys/models/ctr/attentional_factorization_machine.py) | [Jun Xiao et al, 2017. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/abs/1708.04617) | Click Through Rate |\n| [Deep and Cross Network](torecsys/models/ctr/deep_and_cross_network.py) | [Ruoxi Wang et al, 2017. Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123) | Click Through Rate |\n| [Deep Field-Aware Factorization Machine](torecsys/models/ctr/deep_ffm.py) | [Junlin Zhang et al, 2019. FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine](https://arxiv.org/abs/1905.06336) | Click Through Rate |\n| [Deep Factorization Machine](torecsys/models/ctr/deep_fm.py) | [Huifeng Guo et al, 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/abs/1703.04247) | Click Through Rate |\n| [Deep Matching Correlation Prediction](torecsys/models/ctr/deep_mcp.py) | [Wentao Ouyang et al, 2019. Representation Learning-Assisted Click-Through Rate Prediction](https://arxiv.org/pdf/1906.04365.pdf) | Click Through Rate |\n| [Factorization Machine](torecsys/models/ctr/factorization_machine.py) | [Steffen Rendle, 2010. Factorization Machine](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) | Click Through Rate |\n| [Factorization Machine Support Neural Network](torecsys/models/ctr/factorization_machine_supported_neural_network.py) | [Weinan Zhang et al, 2016. Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://arxiv.org/abs/1601.02376) | Click Through Rate |\n| [Field-Aware Factorization Machine](torecsys/models/ctr/field_aware_factorization_machine.py) | [Yuchin Juan et al, 2016. Field-aware Factorization Machines for CTR Prediction](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) | Click Through Rate |\n| [Field-Aware Neural Factorization Machine](torecsys/models/ctr/field_aware_neural_factorization_machine.py) | [Li Zhang et al, 2019. Field-aware Neural Factorization Machine for Click-Through Rate Prediction](https://arxiv.org/abs/1902.09096) | Click Through Rate |\n\n## More About ToR[e]cSys\n\n| Component | Description |\n| --------- | ----------- |\n| [**torecsys.data**] | download sample data, build dataloader, and other functions for convenience |\n| [**torecsys.estimators**] | models with embedding, which can be trained with ```.fit(dataloader)``` directly |\n| [**torecsys.functional**] | functions used in recommendation system |\n| [**torecsys.inputs**] | inputs' functions, including embedding, image transformations |\n| [**torecsys.layers**] | layers-level implementation of algorithms |\n| [**torecsys.losses**] | loss functions used in recommendation system |\n| [**torecsys.metrics**] | metrics to evaluate recommendation system |\n| [**torecsys.models**] | whole-architecture of models which can be trained by **torecsys.base.trainer** |\n| [**torecsys.utils**] | little tools used in torecsys |\n\n(!!! To be confirmed)\n\n### torecsys.models\n\n```torecsys.models``` is a part of model excluding embedding part, so you can choose \\\na suitable embedding method for your model with the following codes:\n\n### torecsys.estimators\n\n```torecsys.estimators``` is another type of model to be used directly if the input \\\nfields and features implemented in the papers are suitable for you:\n\n## Getting Started\n\n(!!! To be confirmed)\n\n### Load Sample data\n\nload the movielens dataset, for example:\n\n### Build Dataset and DataLoader with Sample data\n\n### Use Estimators to train a model\n\n### Make prediction with estimators\n\n## Examples\n\n## Authors\n\n- [Jasper Li](https://github.com) - Developer\n\n## License\n\nToR[e]cSys is MIT-style licensed, as found in the LICENSE file.\n\n\n",
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