Feature embedding in click-through rate prediction
This work addresses feature embedding for click-through rate prediction, which is incremental as it builds on existing models with new modules.
The paper tackled the challenge of improving click-through rate prediction by proposing five feature embedding modules to enhance baseline models, resulting in significant performance gains without major increases in training time.
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.