Improving CTR Prediction with Graph-Enhanced Interest Networks for Sparse Behavior Sequences

Predicting click-through rates is crucial in various fields, including online advertising and recommendation systems. The key to improving the performance of CTR prediction lies in learning a robust user representation, particularly by analyzing their historical behaviors. Previous studies usually model behavior sequences through attention-based sequence models or graph-based methods, which usually struggle to explore diverse latent interests or accurately model user behaviors. Moreover, this challenge is exacerbated when users' historical behaviors are sparse, a common issue in real-world business-to-business (B2B) e-commerce scenarios. In this paper, we propose a novel Graph-Enhanced Interest Network (GEIN) to capture users' latent intents and facilitate the sequential learning of sparse behavior sequences. Specifically, we first construct a hierarchical item-intent heterogeneous graph to enrich the representation of sparse behaviors using diverse information from graphs. Next, we build a user-level behavior interest factor graph to accurately capture user interests. Additionally, a contrastive learning mechanism is incorporated to mitigate the negative robustness impacts caused by sparsity. Extensive experiments on real-world datasets demonstrate that our proposed GEIN outperforms a wide range of state-of-the-art methods. Furthermore, online A/B testing also confirms the superiority of GEIN over competing baselines in a real-world production environment.