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

Logistic Matrix Factorization

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.

Basic Usage

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)

Contents:

Implicit

Basic Usage

Indices and tables