Fast Recommender System on MovieLens 20M Dataset (working in progress)

Inspired by gbolmler implementation of SVD using numba. This repo contains reimplementation of kNN and common matrix factorization methods using numba library to accelarate numpy operations. Numba is a cool library and you need to give this a shot for future implementation using numpy.

MovieLens Dataset

The algorithms in this repo are tested on the Movielens 20M Dataset.

This is a big dataset. In order to extract the dataset to get a smaller dataset, first you need to download MovieLens 20M and save it on your computer, for example, to movielens20M folder. Then you need to create a folder movilens-sample for the new sampling dataset.

On utils/sample_movielens.py you can change the parameter to your like.

if __name__ == "__main__":
    sample_movielens(
       "movielens20M",
       "movielens-sample",
       sample_size=1000
    )

where "movielens20M" is the folder contains MovieLens 20M Dataset, "movielens-sample" is the folder contains new extracted dataset. Size of the extracted dataset can be changed via sample_size.

Netflix Prize Dataset

The algorithms in this repo are also tested on the Netflix Prize dataset. Published by Netflix, the dataset contains a training set of 100 million ratings, which includes a probe set of 1 million ratings. However, the qualifying dataset has not been published anywhere (to my knowledge).

For that reason, the scipt in utils/split_netflix_dataset.py first uses the probe set as the validation set, then split the remaining ratings into training set and testing set. The output contains 3 distinct files, rating_train.csv, rating_test.csv, rating_val.csv just like MovieLens 20M, and can be loaded into the algorithms using utils/DataLoader.

Benchmarks

Folder /examples contains test runs on MovieLens dataset.

Compare to NicolasHug/Surprise, the runtime of kNNBaseline using Pearson similarity scores is much faster (817s compared to 3166s of Surprise on MovieLens 20M dataset).