Maxios : Large Scale Nonnegative Matrix Factorization for Collaborative Filtering

Nonnegative matrix factorization proved useful in many applications, including collaborative filtering – from existing ratings data one would like to predict new product ratings by users. However, factorizing a user-product score matrix is computation and memory intensive. We propose Maxios, a novel approach to fill missing values for large scale and highly sparse matrices efficiently and accurately. We formulate the matrix-completion problem as weighted nonnegative matrix factorization. In addition, we develop distributed update rules using alternating direction method of multipliers. We have implemented the Maxios system on top of Spark, a distributed in-memory computation framework. Experiments on commercial clusters show that Maxios is competitive in terms of scalability and accuracy against the existing solutions on a variety of datasets.