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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 in `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. This library also supports using 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>`_ for `speeding up making recommendations <http://www.benfrederickson.com/approximate-nearest-neighbours-for-recommender-systems/>`_.


Basic Usage
-----------

.. code-block:: python

    import implicit

    # initialize a model
    model = implicit.als.AlternatingLeastSquares(factors=64)

    # train the model on a sparse matrix of user/item/confidence weights
    model.fit(user_item_data)

    # recommend items for a user
    recommendations = model.recommend(userid, user_item_data[userid])

    # find related items
    related = model.similar_items(itemid)


.. toctree::
   :maxdepth: 2
   :caption: Contents:

    Installation <installation>
    Tutorial <tutorial_lastfm>
    API Reference <api/index>
    Resources <resources>

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`