Implicit
=======
[](https://github.com/benfred/implicit/actions?query=workflow%3ABuild+branch%3Amain)
[](https://benfred.github.io/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/).
#### Installation
Implicit can be installed from pypi with:
```
pip install implicit
```
Installing with pip will use prebuilt binary wheels on x86_64 Linux, Windows
and OSX. These wheels include GPU support on Linux.
Implicit can also be installed with conda:
```
# CPU only package
conda install -c conda-forge implicit
# CPU+GPU package
conda install -c conda-forge implicit implicit-proc=*=gpu
```
#### Basic Usage
```python
import implicit
# initialize a model
model = implicit.als.AlternatingLeastSquares(factors=50)
# 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)
```
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](https://benfred.github.io/implicit/).
#### 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 articles about using Implicit to build recommendation systems:
* [H&M Personalized Fashion Recommendations Kaggle Competition](https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/discussion/324129)
* [Yandex Cup 2022: Like Prediction](https://github.com/greenwolf-nsk/yandex-cup-2022-recsys)
* [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 and Python version 3.6 or later.
GPU Support requires at least version 11 of the [NVidia CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
This library is tested with Python 3.7, 3.8, 3.9, 3.10 and 3.11 on Ubuntu, OSX and Windows.
#### Benchmarks
Simple benchmarks comparing the ALS fitting time versus [Spark 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
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"description": "Implicit\n=======\n\n[](https://github.com/benfred/implicit/actions?query=workflow%3ABuild+branch%3Amain)\n[](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\n\nImplicit can be installed from pypi with:\n\n```\npip install implicit\n```\n\nInstalling with pip will use prebuilt binary wheels on x86_64 Linux, Windows\nand OSX. These wheels include GPU support on Linux.\n\nImplicit can also be installed with conda:\n\n```\n# CPU only package\nconda install -c conda-forge implicit\n\n# CPU+GPU package\nconda install -c conda-forge implicit implicit-proc=*=gpu\n```\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 articles about using Implicit to build recommendation systems:\n * [H&M Personalized Fashion Recommendations Kaggle Competition](https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/discussion/324129)\n * [Yandex Cup 2022: Like Prediction](https://github.com/greenwolf-nsk/yandex-cup-2022-recsys)\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.\n\nGPU Support requires at least version 11 of the [NVidia CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).\n\nThis library is tested with Python 3.7, 3.8, 3.9, 3.10 and 3.11 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. 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