Enterprise AI Analysis

MBASR: A Generic Framework for Multi-Behavior Data Augmentation in Sequential Recommendation

Authors: QI XIAO, Shenzhen University, Shenzhen, Guangdong, China; JING XIAO, Shenzhen University, Shenzhen, Guangdong, China; WEIKE PAN, Shenzhen University, Shenzhen, Guangdong, China; ZHONG MING, Shenzhen University, Shenzhen, China, Shenzhen Technology University, Shenzhen, China, and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China

Multi-behavior sequential recommendation (MBSR), which captures sequential patterns and behavioral heterogeneity to model users' multifaceted preferences, has shown promising results. Despite their effectiveness, existing methods often suffer from performance degradation due to inherent data sparsity in real-world scenarios. Current data augmentation methods in recommendation systems predominantly focus on single-behavior modeling, failing to account for the diversity of user preference expressions across different types of behaviors. Moreover, conventional augmentation strategies risk introducing noise or irrelevant patterns during sample generation, potentially distorting the next-item prediction task. To address these challenges, we propose a novel and generic framework called multi-behavior data augmentation for sequential recommendation (MBASR). Specifically, we propose five distinct behavior-aware data augmentation operations, which are designed based on interactions both within and across subsequences, to generate diverse and enriched training samples. Each augmentation operation leverages correlations between behaviors or similarities among users, ensuring that the enhanced data remains aligned with users' natural behavior patterns. Furthermore, we introduce a combined augmentation method, merging two data augmentation operations to achieve better results.