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        "author": "Simone Boglio",
        "author_email": "bogliosimone@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "<p align=\"center\"><img src=\"https://raw.githubusercontent.com/bogliosimone/similaripy/master/logo.png\" alt=\"similaripy\" width=\"350\"/></p>\n\nSimilariPy\n==========\n![PY_PIC]\n[![PYPI_PIC]][PYPI_LINK]\n[![BUILD_STATUS_PIC]][BUILD_STATUS_LINK]\n[![LICENSE_PIC]][LICENSE_LINK]\n[![DOI_PIC]][DOI_LINK]\n\n\n\nThis project provides fast Python implementation of several KNN (K-Nearest Neighbors) similarity algorithms using sparse matrices, useful in Collaborative Filtering Recommender Systems and others.\n\nThe package also include some normalization functions that could be useful in the pre-processing phase before the similarity computation.\n\n#### Similarities\n\nBase similarity models:\n * Dot Product\n * Cosine\n * Asymmetric Cosine\n * Jaccard\n * Dice\n * Tversky\n\n Graph-based similarity models:\n * P3&alpha;\n * RP3&beta;\n\n Advanced similarity model:\n * S-Plus\n\n[Similarities Documentation](https://github.com/bogliosimone/similaripy/blob/master/guide/temp_guide.md)\n\nAll models have multi-threaded routines, using Cython and OpenMP to fit the models in parallel among all available CPU cores.\n\n#### Normalizations\n\nThe package contains normalization functions like: l1, l2, max, tf-idf, bm25, bm25+.\n\nAll the functions are compiled at low-level and could operate in-place, on csr-matrixes, if you need to save memory.\n\nFor tf-idf, bm25, bm25+ you could chose the log-base and how the term-frequency (TF) and the inverse document frequency (IDF) are computed.\n\n#### Installation and usage\n\nTo install:\n\n```cmd\npip install similaripy\n```\n\nBasic usage:\n\n```python\nimport similaripy as sim\nimport scipy.sparse as sps\n\n# create a random user-rating matrix (URM)\nurm = sps.random(1000, 2000, density=0.025)\n\n# normalize matrix with bm25\nurm = sim.normalization.bm25(urm)\n\n# train the model with 50 knn per item \nmodel = sim.cosine(urm.T, k=50)\n\n# recommend 100 items to users 1, 14 and 8 filtering the items already seen by each users\nuser_recommendations = sim.dot_product(urm, model.T, k=100, target_rows=[1,14,8], filter_cols=urm)\n\n```\n\n#### Requirements\n\n| Package                         | Version        |\n| --------------------------------|:--------------:|   \n| numpy                           |   >= 1.14      |   \n| scipy                           |   >= 1.0.0     |\n| tqdm                            |   >= 4.19.6    |\n| cython                          |   >= 0.28.1    |\n\n\nNOTE: In order to compile the Cython code it is required a GCC compiler with OpenMP \n(on OSX it can be installed with homebrew: ```brew install gcc```).\n\nThis library has been tested with Python 3.6 on Ubuntu, OSX and Windows.\n\n(Note: on Windows there are problem with flag *format_output='csr'*, just let it equals to the default value *'coo'*)\n\n#### Optimal Configuration\n\nI recommend configuring SciPy/Numpy to use Intel's MKL matrix libraries.\nThe easiest way of doing this is by installing the Anaconda Python distribution.\n\n#### Future work\n\nI plan to release in the next future some utilities:\n- Utilities for sparse matrices\n- New similarity functions ( good ideas are welcome :)  )\n\n#### History\nThe idea of build this library comes from the **[RecSys Challenge 2018](https://recsys-challenge.spotify.com)** organized by Spotify. \n\nMy team, the Creamy Fireflies, had problem in compute very huge similarity models in a reasonable time (66 million of interactions in the user-rating matrix) and using python and numpy were not suitable since a full day was required to compute one single model.\n\nAs a member of the the team I spent a lot of hours to develop these high-performance similarities in Cython to overcome the problem. At the end of the competition, pushed by my team friends, I decide to release my work to help people that one day will encounter our same problem.\n\nThanks to my Creamy Fireflies friends for support me.\n\n#### License\nReleased under the MIT License\n\nCitation information: [![DOI_PIC]][DOI_LINK]\n\n```\n@misc{boglio_simone_similaripy,\n  author       = {Boglio Simone},\n  title        = {bogliosimone/similaripy},\n  doi          = {10.5281/zenodo.2583851},\n  url          = {https://doi.org/10.5281/zenodo.2583851}\n}\n```\n\n[DOI_PIC]: https://zenodo.org/badge/DOI/10.5281/zenodo.2583851.svg\n[DOI_LINK]: https://doi.org/10.5281/zenodo.2583851\n[LICENSE_PIC]: https://img.shields.io/github/license/bogliosimone/similaripy.svg\n[LICENSE_LINK]: https://github.com/bogliosimone/similaripy/blob/master/LICENSE\n[PYPI_PIC]: https://img.shields.io/pypi/v/similaripy.svg\n[PYPI_LINK]: https://pypi.org/project/similaripy/\n[PY_PIC]: https://img.shields.io/pypi/pyversions/similaripy.svg\n[BUILD_STATUS_PIC]: https://travis-ci.org/bogliosimone/similaripy.svg?branch=master\n[BUILD_STATUS_LINK]: https://travis-ci.org/bogliosimone/similaripy\n\n\n\n",
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        "keywords": "Similarity,KNN,Nearest Neighbors,Collaborative Filtering,Normalization,Recommender Systems",
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        "name": "similaripy",
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