  We tackle the challenge of feature embedding for the purposes of improving
the click-through rate prediction process. We select three models: logistic
regression, factorization machines and deep factorization machines, as our
baselines and propose five different feature embedding modules: embedding
scaling, FM embedding, embedding encoding, NN embedding and the embedding
reweighting module. The embedding modules act as a way to improve baseline
model feature embeddings and are trained alongside the rest of the model
parameters in an end-to-end manner. Each module is individually added to a
baseline model to obtain a new augmented model. We test the predictive
performance of our augmented models on a publicly accessible dataset used for
benchmarking click-through rate prediction models. Our results show that
several proposed embedding modules provide an important increase in predictive
performance without a drastic increase in training time.
