DCE-MBR: Multi-behavior recommendation via hierarchical denoising and cascade enhancement

Graph neural networks (GNNs) have shown considerable promise in multi-behavior recommendation tasks, particularly for target behavior prediction (e.g., purchase conversion), by effectively integrating auxiliary behavioral signals (e.g., item browsing, cart addition). Recent advances in the field have substantially improved the modeling of hierarchical interactions and multi-behavior dependencies, effectively mitigating foundational challenges such as data sparsity. However, these state-of-the-art methods frequently overlook the semantic heterogeneity and inherent noise within auxiliary interactions, often resorting to uniform or simplistic denoising strategies that risk discarding valuable signals. To overcome this persistent limitation, the Denoising Cascade-Enhanced Multi-Behavior Recommendation (DCE-MBR) framework is introduced. DCE-MBR is designed to simultaneously suppress noise and preserve semantically informative interactions through a dual-stage architecture. First, a hierarchical graph denoising module dynamically removes noisy edges by applying behavior-specific thresholds across multiple levels of granularity, thereby preserving essential neighbor relations. Next, a cascade-enhanced module incrementally refines user preferences by propagating target behavior signals through auxiliary behavior paths, leading to improved feature representations. Comprehensive evaluations based on the Taobao as well as Tmall data collections show that DCE-MBR outperforms state-of-the-art baselines, achieving relative gains of 18.66% in Hit@10 and 15.42% in NDCG@10. These results confirm the model’s robustness against noisy interactions and its effectiveness in capturing intricate multi-behavior dependencies. The source code is publicly available at DCEMBR 1 .