Robust Factorization Machines for User Response Prediction

Factorization machines (FMs) are a state-of-the-art model class for user response prediction in the computational advertising domain. Rapid growth of internet and mobile device usage has given rise to multiple customer touchpoints. This coupled with factors like high cookie churn rate results in a fragmented view of user activity at the advertiser's end. Current literature assumes procured user signals as the absolute truth, which is contested by the absence of deterministic identity linkage across a user's multiple avatars. In this work, we characterize the data uncertainty using Robust Optimization (RO) paradigm to design approaches that are immune against perturbations. We propose two novel algorithms: robust factorization machine (RFM) and its field-aware variant (RFFM), under interval uncertainty. These formulations are generic and can find applicability in any classification setting under noise. We provide a distributed and scalable Spark implementation using parallel stochastic gradient descent. In the experiments conducted on three real-world datasets, the robust counterparts outperform the baselines significantly under perturbed settings. Our experimental findings reveal interesting connections between choice of uncertainty set and the noise-pro ofness of resulting models.