IO-aware Factorization Machine for User Response Prediction

As a supervised learning method, Factorization Machine (FM) is famous for its capability of modeling feature interactions. However, FM's performance might be bad if we assign the same weight to all feature interactions, as not all of them are equally useful and productive. Attentional Factorization Machine (AFM) improves FM by discriminating the importance of distinctive feature interactions via a neural attention network. Nevertheless, the neural attention network in AFM is not fine-grained enough and it ignores the information of the fields implied by the features, which limits the performance of the model. In this work, we propose a novel model named IO-aware Factorization Machine (IOFM), which enhances the feature representation ability of attention mechanism in estimating weights via two awareness auxiliary matrices. To make the model more efficient, we further reduce the model parameters using canonical decomposition for the two auxiliary matrices and design a shared matrix to correlate the decomposed matrices. Extensive experiments on two real-world datasets indicate the superiority of our IOFM model over the state-of-the-art methods.