Neural Statistics for Click-Through Rate Prediction

Abstract

With the success of deep learning, click-through rate (CTR) predictions are transitioning from shallow approaches to deep architectures. Current deep CTR prediction usually follows the Embedding & MLP paradigm, where the model embeds categorical features into latent semantic space. This paper introduces a novel embedding technique called neural statistics that instead learns explicit semantics of categorical features by incorporating feature engineering as an innate prior into the deep architecture in an end-to-end manner. Besides, since the statistical information changes over time, we study how to adapt to the distribution shift in the MLP module efficiently. Offline experiments on two public datasets validate the effectiveness of neural statistics against state-of-the-art models. We also apply it to a large-scale recommender system via online A/B tests, where the user's satisfaction is significantly improved.