Dimension Relation Modeling for Click-Through Rate Prediction

Abstract

Embedding mechanism plays an important role in Click-Through-Rate (CTR) prediction. Essentially, it tries to learn a new feature space with some learned latent properties as the basis, and maps the high dimensional and categorical raw data to dense, rich and expressive representations, i.e., the embedding features. Current researches usually focus on learning the interactions through operations on the whole embedding features without considering the relations among the learned latent properties. In this paper, we find it has clear positive effects on CTR prediction to model such relations and propose a novel Dimension Relation Module (DRM) to capture them through dimension recalibration. We show that DRM can improve the performance of existing models consistently and the improvements are more obvious when the embedding dimension is higher. We further boost Field-wise and Element-wise embedding methods with our DRM and name this new model FED network. Extensive experiments demonstrate that FED is very powerful in CTR prediction task and achieves new state-of-the-art results on Criteo, Avazu and JD.com datasets.