Metadata-Version: 2.1
Name: implicit
Version: 0.4.4
Summary: Collaborative Filtering for Implicit Datasets
Home-page: http://github.com/benfred/implicit/
Author: Ben Frederickson
Author-email: ben@benfrederickson.com
License: MIT
Description: Implicit
        =======
        
        [![Build Status](https://travis-ci.org/benfred/implicit.svg?branch=master)](https://travis-ci.org/benfred/implicit)
        [![Windows Build Status](https://ci.appveyor.com/api/projects/status/9kfbvx5i6dc48yr0?svg=true)](https://ci.appveyor.com/project/benfred/implicit)
        
        Fast Python Collaborative Filtering for Implicit Datasets.
        
        This project provides fast Python implementations of several different popular recommendation algorithms for
        implicit feedback datasets:
        
         * 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
        Feedback Collaborative Filtering](https://pdfs.semanticscholar.org/bfdf/7af6cf7fd7bb5e6b6db5bbd91be11597eaf0.pdf).
        
         * [Bayesian Personalized Ranking](https://arxiv.org/pdf/1205.2618.pdf).
        
         * [Logistic Matrix Factorization](https://web.stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf)
        
         * Item-Item Nearest Neighbour models using Cosine, TFIDF or BM25 as a distance metric.
        
        All models have multi-threaded training routines, using Cython and OpenMP to fit the models in
        parallel among all available CPU cores.  In addition, the ALS and BPR models both have custom CUDA
        kernels - enabling fitting on compatible GPU's. Approximate nearest neighbours libraries such as [Annoy](https://github.com/spotify/annoy), [NMSLIB](https://github.com/searchivarius/nmslib)
        and [Faiss](https://github.com/facebookresearch/faiss) can also be used by Implicit to [speed up
        making recommendations](https://www.benfrederickson.com/approximate-nearest-neighbours-for-recommender-systems/).
        
        To install:
        
        ```
        pip install implicit
        ```
        
        Basic usage:
        
        ```python
        import implicit
        
        # initialize a model
        model = implicit.als.AlternatingLeastSquares(factors=50)
        
        # train the model on a sparse matrix of item/user/confidence weights
        model.fit(item_user_data)
        
        # recommend items for a user
        user_items = item_user_data.T.tocsr()
        recommendations = model.recommend(userid, user_items)
        
        # find related items
        related = model.similar_items(itemid)
        ```
        
        The examples folder has a program showing how to use this to [compute similar artists on the
        last.fm dataset](https://github.com/benfred/implicit/blob/master/examples/lastfm.py).
        
        For more information see the [documentation](http://implicit.readthedocs.io/).
        
        #### Articles about Implicit
        
        These blog posts describe the algorithms that power this library:
        
         * [Finding Similar Music with Matrix Factorization](https://www.benfrederickson.com/matrix-factorization/)
         * [Faster Implicit Matrix Factorization](https://www.benfrederickson.com/fast-implicit-matrix-factorization/)
         * [Implicit Matrix Factorization on the GPU](https://www.benfrederickson.com/implicit-matrix-factorization-on-the-gpu/)
         * [Approximate Nearest Neighbours for Recommender Systems](https://www.benfrederickson.com/approximate-nearest-neighbours-for-recommender-systems/)
         * [Distance Metrics for Fun and Profit](https://www.benfrederickson.com/distance-metrics/)
        
        There are also several other blog posts about using Implicit to build recommendation systems:
        
         * [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)
         * [Intro to Implicit Matrix Factorization: Classic ALS with Sketchfab Models](http://blog.ethanrosenthal.com/2016/10/19/implicit-mf-part-1/)
         * [A Gentle Introduction to Recommender Systems with Implicit Feedback](https://jessesw.com/Rec-System/).
        
        
        #### Requirements
        
        This library requires SciPy version 0.16 or later. Running on OSX requires an OpenMP compiler,
        which can be installed with homebrew: ```brew install gcc```. Running on Windows requires Python
        3.5+.
        
        GPU Support requires at least version 9 of the [NVidia CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). The build will use the ```nvcc``` compiler
        that is found on the path, but this can be overriden by setting the CUDAHOME enviroment variable
        to point to your cuda installation.
        
        This library has been tested with Python 2.7, 3.5, 3.6 and 3.7 on Ubuntu and OSX, and tested with
        Python 3.5 and 3.6 on Windows.
        
        #### Benchmarks
        
        Simple benchmarks comparing the ALS fitting time versus [Spark and QMF can be found here](https://github.com/benfred/implicit/tree/master/benchmarks).
        
        #### Optimal Configuration
        
        I'd recommend configuring SciPy to use Intel's MKL matrix libraries. One easy way of doing this is by installing the Anaconda Python distribution.
        
        For systems using OpenBLAS, I highly recommend setting 'export OPENBLAS_NUM_THREADS=1'. This
        disables its internal multithreading ability, which leads to substantial speedups for this
        package. Likewise for Intel MKL, setting 'export MKL_NUM_THREADS=1' should also be set.
        
        Released under the MIT License
        
Keywords: Matrix Factorization,Implicit Alternating Least Squares,Collaborative Filtering,Recommender Systems
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Cython
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
