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.
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.
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)