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 and in Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering.
• Bayesian Personalized Ranking
• 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, NMSLIB and Faiss for speeding up making recommendations.

Installation

To install:

pip install implicit

Basic Usage

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)

Articles about Implicit

These blog posts describe the algorithms that power this library:

• Finding Similar Music with Matrix Factorization
• Faster Implicit Matrix Factorization
• Implicit Matrix Factorization on the GPU
• Approximate Nearest Neighbours for Recommender Systems
• Distance Metrics for Fun and Profit

There are also several other blog posts about using Implicit to build recommendation systems:

• Recommending GitHub Repositories with Google BigQuery and the implicit library
• Intro to Implicit Matrix Factorization: Classic ALS with Sketchfab Models
• A Gentle Introduction to Recommender Systems with Implicit Feedback

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.