Dual-Interest Adaptive Network for Click-Through Rate Prediction

In advertising recommendation systems, click-through rate (CTR) prediction is a critical task. Capturing users' interests from their rich historical behaviors is key to improving prediction results. Although traditional deep learning methods can capture users' interests to some extent, they fail to account for the local and global interests reflected in users' historical behaviors and the dynamic relationships between them. In this paper, we propose a novel architecture - Dual-Interest Adaptive Network (DIAN), which adaptively extracts both local and global interests of users. Specifically, to better explore users' interests in depth, we propose an Adaptive Interest Extraction Block applied to users' historical behavior sequences. By introducing an attention mechanism, this module can flexibly allocate weights to users' local and global interests after decoupling user behaviors. Additionally, to capture complex feature interactions, our model introduces two feature extractors: one combines a Multi-Layer Perceptron (MLP) with a Cross Network for high-order feature extraction, and the other incorporates an Attention Factorization Machine (AFM) for low-order feature extraction. We conducted extensive experiments on the Movielens-1M and Amazon Electronics datasets, validating the effectiveness of DIAN.