Benchmarks

In this folder we show benchmarks using different algorithms. To facilitate the benchmark computation, we provide a set of wrapper functions that can be found in the file benchmark_utils.py.

MovieLens

MovieLens is one of the most common datasets used in the literature in Recommendation Systems. The dataset consists of a collection of users, movies and movie ratings, there are several available sizes:

• MovieLens 100k: 100,000 ratings from 1000 users on 1700 movies.
• MovieLens 1M: 1 million ratings from 6000 users on 4000 movies.
• MovieLens 10M: 10 million ratings from 72000 users on 10000 movies.
• MovieLens 20M: 20 million ratings from 138000 users on 27000 movies

The MovieLens benchmark can be seen at movielens.ipynb. This illustrative comparison applies to collaborative filtering algorithms available in this repository such as Spark ALS, SVD, SAR, LightGCN and others using the Movielens dataset, using three environments (CPU, GPU and Spark). These algorithms are usable in a variety of recommendation tasks, including product or news recommendations.