W2FM: The Doubly-Warped Factorization Machine

Factorization Machines (FMs) enhance an underlying linear regression or classification model by capturing feature interactions. Intuitively, FMs warp the feature space to help capture the underlying non-linear structure of the machine learning task. In this paper, we propose novel Doubly-Warped Factorization Machines (or W2FM s) that leverage multiple complementary space warping strategies to improve the representational ability of FMs. Our approach abstracts the feature interaction in FMs as additional affine transformations (thus warping the space), which can be learned efficiently without introducing large numbers of model parameters. We also explore alternative W2FM based approaches and conduct extensive experiments on real world data sets. These experiments show that W2FM achieves better performance in collaborative filtering task not only relative to vanilla FMs, but also against other state-of-the-art competitors, such as Attention FM (AFM), Holographic FM (HFM), and Neural FM (NFM).