AEMS: Adaptive Ensemble GNNs for Multibehavior Stream Recommendation

Traditional systems often focus on a singular action type, such as a purchase, while overlooking the rich behavioral pluralism that is inherent in user engagement, such as the transient clicks or latent purchase intents signaled by cart additions. This narrow focus leads to sparse datasets and hinders recommendation accuracy. In response to the evolving landscape of real-time recommendation systems, contemporary frameworks are increasingly leveraging dynamic user engagement streams that span actions, such as purchasing, item retention, and exploratory browsing, to refine suggestions. To bridge this gap, we introduce a novel and revolutionary model: adaptive ensemble graph neural networks (GNNs), for multibehavior stream recommendation (AEMS). The AEMS synergizes long-term preference patterns (derived from historical interactions) with real-time user intents and item attributes (captured through multibehavior signals), integrating them via an adaptive ensemble neural gating mechanism. This design effectively fuses multifaceted behaviors and addresses the challenges of data sparsity and heterogeneity. This architecture is further enhanced by a behavior-aware training paradigm that dynamically weights heterogeneous interactions, such as clicks, add-to-cart actions, likes, and other behavioral signals, to increase its recommendation precision. Extensive experiments conducted on real-world datasets demonstrate that the AEMS significantly outperforms the state-of-the-art methods, demonstrating its ability to decode complex, high-order synergies in streaming environments.