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    "info": {
        "author": "Ben Frederickson",
        "author_email": "ben@benfrederickson.com",
        "bugtrack_url": null,
        "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",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Implicit\n=======\n\n[![Build\nStatus](https://github.com/benfred/implicit/workflows/Build/badge.svg)](https://github.com/benfred/implicit/actions?query=workflow%3ABuild+branch%3Amain)\n[![Documentation](https://img.shields.io/badge/documentation-blue.svg)](https://benfred.github.io/implicit/)\n\n\nFast Python Collaborative Filtering for Implicit Datasets.\n\nThis project provides fast Python implementations of several different popular recommendation algorithms for\nimplicit feedback datasets:\n\n * Alternating Least Squares as described in the papers [Collaborative Filtering for Implicit Feedback Datasets](http://yifanhu.net/PUB/cf.pdf) and [Applications of the Conjugate Gradient Method for Implicit\nFeedback Collaborative Filtering](https://pdfs.semanticscholar.org/bfdf/7af6cf7fd7bb5e6b6db5bbd91be11597eaf0.pdf).\n\n * [Bayesian Personalized Ranking](https://arxiv.org/pdf/1205.2618.pdf).\n\n * [Logistic Matrix Factorization](https://web.stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf)\n\n * Item-Item Nearest Neighbour models using Cosine, TFIDF or BM25 as a distance metric.\n\nAll models have multi-threaded training routines, using Cython and OpenMP to fit the models in\nparallel among all available CPU cores.  In addition, the ALS and BPR models both have custom CUDA\nkernels - enabling fitting on compatible GPU's. Approximate nearest neighbours libraries such as [Annoy](https://github.com/spotify/annoy), [NMSLIB](https://github.com/searchivarius/nmslib)\nand [Faiss](https://github.com/facebookresearch/faiss) can also be used by Implicit to [speed up\nmaking recommendations](https://www.benfrederickson.com/approximate-nearest-neighbours-for-recommender-systems/).\n\n#### Installation\nThere are binary packages on conda-forge for Linux, Windows and OSX. These can be installed with:\n\n```\nconda install -c conda-forge implicit\n```\n\nThere are also GPU enabled packages on conda-forge for x86_64 Linux systems using either CUDA\n11.0, 11.1 or 11.2. The GPU packages can be installed with:\n\n```\nconda install -c conda-forge implicit implicit-proc=*=gpu\n```\nThere is also an sdist package on PyPi. This package can be installed with:\n\n```\npip install implicit\n```\n\nNote that installing with pip requires a C++ compiler to be installed on your system, since this\nmethod will build implicit from source.\n\n#### Basic Usage\n\n```python\nimport implicit\n\n# initialize a model\nmodel = implicit.als.AlternatingLeastSquares(factors=50)\n\n# train the model on a sparse matrix of user/item/confidence weights\nmodel.fit(user_item_data)\n\n# recommend items for a user\nrecommendations = model.recommend(userid, user_item_data[userid])\n\n# find related items\nrelated = model.similar_items(itemid)\n```\n\nThe examples folder has a program showing how to use this to [compute similar artists on the\nlast.fm dataset](https://github.com/benfred/implicit/blob/master/examples/lastfm.py).\n\nFor more information see the [documentation](https://benfred.github.io/implicit/).\n\n#### Articles about Implicit\n\nThese blog posts describe the algorithms that power this library:\n\n * [Finding Similar Music with Matrix Factorization](https://www.benfrederickson.com/matrix-factorization/)\n * [Faster Implicit Matrix Factorization](https://www.benfrederickson.com/fast-implicit-matrix-factorization/)\n * [Implicit Matrix Factorization on the GPU](https://www.benfrederickson.com/implicit-matrix-factorization-on-the-gpu/)\n * [Approximate Nearest Neighbours for Recommender Systems](https://www.benfrederickson.com/approximate-nearest-neighbours-for-recommender-systems/)\n * [Distance Metrics for Fun and Profit](https://www.benfrederickson.com/distance-metrics/)\n\nThere are also several other blog posts about using Implicit to build recommendation systems:\n\n * [Recommending GitHub Repositories with Google BigQuery and the implicit library](https://medium.com/@jbochi/recommending-github-repositories-with-google-bigquery-and-the-implicit-library-e6cce666c77)\n * [Intro to Implicit Matrix Factorization: Classic ALS with Sketchfab Models](http://blog.ethanrosenthal.com/2016/10/19/implicit-mf-part-1/)\n * [A Gentle Introduction to Recommender Systems with Implicit Feedback](https://jessesw.com/Rec-System/).\n\n\n#### Requirements\n\nThis library requires SciPy version 0.16 or later and Python version 3.6 or later.\nRunning on OSX requires an OpenMP compiler, which can be installed with homebrew: ```brew install gcc```.\n\nGPU Support requires at least version 11 of the [NVidia CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). The build will use the ```nvcc``` compiler\nthat is found on the path, but this can be overridden by setting the CUDAHOME environment variable\nto point to your cuda installation.\n\nThis library has been tested with Python 3.6, 3.7, 3.8 and 3.9 on Ubuntu, OSX and Windows.\n\n#### Benchmarks\n\nSimple benchmarks comparing the ALS fitting time versus [Spark can be found here](https://github.com/benfred/implicit/tree/master/benchmarks).\n\n#### Optimal Configuration\n\nI'd recommend configuring SciPy to use Intel's MKL matrix libraries. One easy way of doing this is by installing the Anaconda Python distribution.\n\nFor systems using OpenBLAS, I highly recommend setting 'export OPENBLAS_NUM_THREADS=1'. This\ndisables its internal multithreading ability, which leads to substantial speedups for this\npackage. Likewise for Intel MKL, setting 'export MKL_NUM_THREADS=1' should also be set.\n\nReleased under the MIT License\n\n\n",
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