Sketching Techniques for Very Large Matrix Factorization

Abstract

Matrix factorization is a prominent technique for approximate matrix reconstruction and noise reduction. Its common appeal is attributed to its space efficiency and its ability to generalize with missing information. For these reasons, matrix factorization is central to collaborative filtering systems. In the real world, such systems must deal with million of users and items, and they are highly dynamic as new users and new items are constantly added. Factorization techniques, however, have difficulties to cope with such a demanding environment. Whereas they are well understood with static data, their ability to efficiently cope with new and dynamic data is limited. Scaling to extremely large numbers of users and items is also problematic. In this work, we propose to use the count sketching technique for representing the latent factors with extreme compactness, facilitating scaling.