Deep Interest Network for Click-Through Rate Prediction

Display advertising business brings billions dollars income yearly in Alibaba. In cost-per-click (CPC) advertising system, advertisements are ranked by the eCPM (effective cost per mille) which is the product of the bid price and CTR (click-through rate). Hence, the performance of CTR prediction model has a straight impact on the final revenue and plays a key role in the advertising system. Driven by the success of deep learning in image recognition, computer vision and natural language processing, a number of deep learning based methods have been proposed for CTR prediction task [1, 2, 3, 4]. These methods usually first employ embedding layer on the input, mapping original large scale sparse id features to the distributed representations, then add fully connected layers (in other words, multilayer perceptrons, MLPs) to automatically learn the nonlinear relations among features. Compared to traditional commonly used logistic regression model [5, 6]. MLPs can reduce a lot of feature engineering jobs, which is time and manpower consuming in industry applications. MLPs now have become a popular model structure on CTR prediction problem. However, in the fields with rich internet-scale user behavior data, such as online advertising and recommendation system in e-commence industry, these MLPs models often lack of deep understanding and exploiting the specific structures of behavior data, thus leave space for further improvement.