Study on Interpretable Click-Through Rate Prediction Based on Attention Mechanism

Click-Through Rate（CTR） prediction is critical to recommender systems.The improvement of CTR prediction can directly affect the earnings target of the recommender system.The performance and interpretation of the CTR prediction algorithm can guide developers to understand and evaluate recommender system accurately.That's also helpful for system design.Most existing approaches are based on linear feature interaction and deep feature extraction, which have poor model interpretation in the outcomes.Moreover, very few previous studies were conducted on the model interpretation of the CTR prediction.Therefore, in this paper, we propose a novel model which introduces multi-head self-attention mechanism to the embedding layer, the linear feature interaction component and the deep component, to study the model interpretation.We propose two models for the deep component.One is deep neural networks（DNN） enhanced by multi-head self-attention mechanism, the other computes high-order feature interaction by stacking multipl