Incremental Multi-Behavior Recommendation

Multi-behavior recommendation, unlike traditional single-behavior recommendation focusing solely on the target behavior (e.g., purchase), exploits multiple types of user-item interactions (e.g., view, favorite, add-to-cart, purchase) to mitigate data sparsity and learn more comprehensive user preferences. However, real-world data is inherently transmitted in a streaming manner, and most existing multi-behavior recommendation models are trained on static offline datasets, which cannot efficiently handle continuously arriving data streams. Full retraining incurs high computational cost and long training time. In contrast, fine-tuning is efficient but prone to ''catastrophic forgetting'', resulting in performance degradation. Incremental recommendation techniques offer a potential solution, but the existing methods are mostly designed for cases with one single behavior, ignoring valuable information contained in heterogeneous behaviors. To fill this gap, we introduce a new and important problem, i.e., incremental multi-behavior recommendation (IMBR), requiring a model to handle streaming data while utilizing valuable heterogeneous behaviors. To this end, we propose a novel solution called Behavior-aware Incremental Graph Convolution Network with Multi-Task Learning (BIGCN-MTL) for IMBR. Specifically, our BIGCN-MTL contains two key modules: behavior-aware incremental graph convolution network (BIGCN), which employs incremental cascading graph convolution to preserve both historical neighborhood information and behavior cascading dependency, and historical interest distillation (HID), which maintains users' preference stability and prevents behavioral semantic forgetting via non-interest consistency constraint and preference contrastive distillation. Extensive experiments on four real-world datasets demonstrate that our BIGCN-MTL achieves superior recommendation performance and improves training efficiency compared to full retraining. The source code of our BIGCN-MTL is available at https://github.com/codingPeter1/BIGCN-MTL.