A Generic Behavior-Aware Data Augmentation Framework for Sequential Recommendation

Multi-behavior sequential recommendation (MBSR), which models multi-behavior sequentiality and heterogeneity to better learn users' multifaceted intentions has achieved remarkable success. Though effective, the performance of these approaches may be limited due to the sparsity inherent in a real-world data. Existing data augmentation methods in recommender systems focus solely on a single type of behavior, overlooking the variations in expressing user preferences via different types of behaviors. During the augmentation of samples, it is easy to introduce excessive disturbance or noise, which may mislead the next-item recommendation. To address this limitation, we propose a novel generic framework called multi-behavior data augmentation for sequential recommendation (MBASR). Specifically, we design three behavior-aware data augmentation operations to construct rich training samples. Each augmentation operation takes into account the correlations between behaviors and aligns with the users' behavior patterns. In addition, we introduce a position-based sampling strategy that can effectively reduce the perturbation brought by the augmentation operations to the original data. Note that our model is data-oriented and can thus be embedded in different downstream MBSR models, so the overall framework is generic. Extensive experiments on three real-world datasets demonstrate the effectiveness of our MBASR and its applicability to a wide variety of mainstream MBSR models. Our source code is available at https://github.com/XiaoJing-C/MBASR.