Efficient Mixed Dimension Embeddings for Matrix Factorization

Despite the prominence of neural network approaches in the field of recommender systems, simple methods such as matrix factorization with quadratic loss are still used in industry for several reasons. These models can be trained with alternating least squares, which makes them easy to implement in a massively parallel manner, thus making it possible to utilize billions of events from real-world datasets. Large-scale recommender systems need to account for severe popularity skew in the distributions of users and items, so a lot of research is focused on implementing sparse, mixed dimension or shared embeddings to reduce both the number of parameters and overfitting on rare users and items. In this paper we propose two matrix factorization models with mixed dimension embeddings, which can be optimized in a massively parallel fashion using the alternating least squares approach.