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        "author": "Shuyang Li",
        "author_email": "shuyangli94@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
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            "Intended Audience :: Education",
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            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
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        "description": "# RecSysModels\nHere we have implemented various Recommender System algorithms for implicit feedback and sequential recommendation. These algorithms are implemented in Python and [TensorFlow](https://www.tensorflow.org). This package aims to provide clear, annotated, and efficient implementations of these algorithms along with wrapper classes and methods for easy experimentation and usage.\n\n## Implicit Feedback\nThis package focuses on recommendations based on sequential and [implicit feedback](http://yifanhu.net/PUB/cf.pdf). In these settings there is no explicit numerical rating of items by users - only the record of actions they have taken. Thus there is only observed positive feedback - if a user `u` has not interacted with item `i`, it could either be because they dislike the item (negative) or they merely have not come upon this item yet (positive).\n\nThe algorithms implemented here approach the implicit feedback recommendation problem from a pairwise ranking perspective, where we assume that an item a user has interacted with should be ranked higher than an item that the user has not yet interacted with.\n\n## Algorithms Implemented\n- Bayesian Personalized Ranking (__BPR__), from ['BPR: Bayesian Personalized Ranking from Implicit Feedback'](https://arxiv.org/abs/1205.2618) (Rendle et al. 2009)\n- Factorized Personalized Markov Chains (__FPMC__), from ['Factorizing personalized Markov chains for next-basket recommendation'](https://dl.acm.org/citation.cfm?id=1772773) (Rendle et al. 2010)\n- __TransRec__, from ['Translation-based Recommendation'](https://arxiv.org/abs/1707.02410) (He, et al. 2017)\n\n## Installation\n`RecSysModels` is on [`PyPI`](https://pypi.org/), so you can install the package with `pip`:\n```bash\n$ pip install recsys_models\n```\n\n## Dependencies\n- [`Python 3+`](https://www.python.org/) (3.6 may be required for Tensorflow-GPU on Windows)\n- [`tensorflow`](https://www.tensorflow.org/install/) or [`tensorflow-gpu`](https://www.tensorflow.org/install/gpu)\n- [`numpy`](http://www.numpy.org/)\n- [`pandas`](https://pandas.pydata.org/pandas-docs/stable/index.html)\n- [`Jupyter`/`JupyterLab`](https://jupyter.org/) (If you want to run the notebook)\n\n## Sample Usage\nSee the [`sample_pipeline Jupyter Notebook`](https://github.com/shuyangli94/RecSysModels/blob/master/sample_pipeline.ipynb) for sample usage. In order to run this, you will need to download the [MovieLens 1M Dataset](https://grouplens.org/datasets/movielens/1m/) released in 2003 by the wonderful folks at the [GroupLens Lab](https://grouplens.org/) at the University of Minnesota.\n\n## Interoperability\nFor interoperability, this package supports initializing a model with pretrained weights in the form of `numpy` arrays exported from models trained under other frameworks. Please see individual model files (e.g. [BPR](https://github.com/shuyangli94/RecSysModels/blob/master/recsys_models/models/bpr.py)) for a description of trainable variables and their shapes.\n\n\n###### This package is released under [GNU GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html) by [Shuyang Li](http://shuyangli.me/)\n\n",
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        "keywords": "recommender systems,recommender,recommendation system,tensorflow",
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        "name": "recsys-models",
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