Deep Adaptive Interest Network for CTR Prediction

Click-Through Rate (CTR) prediction is important in many industrial applications, such as E-commerce, news, and information. Understanding sophisticated feature interactions behind users’ behaviors is essential for CTR prediction. Although existing methods have made significant improvements, there exist some problems: (1) only concentrate on modeling implicit information from the user side, while ignoring the interest hidden in the historical interactive behaviors; (2) insufficient feature extraction, only focusing on high-order feature interactions or low-order feature interactions. To overcome these limitations, we propose a Deep Adaptive Interest Network (DAIN) for CTR Prediction in the local and global views, respectively. Specifically, to extract user’s interest, we first develop a local attention mechanism applied to the user behaviors and candidate ads, which can adaptively calculate users’ interest representation given a candidate ad in the local views. To capture feature interactions, we propose a feature interaction extractor containing Multi-layer Perceptrons (MLP) and Factorization Machines (FM) components to capture high-order and low-order feature interactions. To adaptively learn the influences of high-order and low-order feature interactions on the target item, we finally employ a linear-based global attention mechanism in the feature interaction extractor. The effectiveness of DAIN is verified by comprehensive experiments on three datasets.