通过行为级数据增强与双融合建模，提升多行为序列推荐效果。
BLADE: A Behavior-Level Data Augmentation Framework with Dual Fusion Modeling for Multi-Behavior Sequential Recommendation
- 双融合架构在输入和中间层融合行为信息，捕捉用户多维偏好。
- 设计三种行为级数据增强方法，缓解数据稀疏问题。
- 适合关注多行为推荐、序列建模的算法研究者。
多行为序列推荐旨在通过建模用户随时间变化的多样化交互来捕捉动态兴趣。尽管已有研究探索该方向，但推荐性能仍不理想，主要受限于行为异质性和数据稀疏性两大挑战。为此，我们提出BLADE框架，在增强多行为建模的同时缓解数据稀疏问题。具体地，为应对行为异质性，引入双项-行为融合架构，在输入层与中间层同时融入行为信息，实现多视角偏好建模；为缓解数据稀疏性，设计三种直接作用于行为序列的行为级数据增强方法，生成多样化的增强视图，同时保持物品序列语义一致性。这些增强视图通过对比学习进一步提升表征学习与泛化能力。在三个真实数据集上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Multi-behavior sequential recommendation aims to capture users' dynamic interests by modeling diverse types of user interactions over time. Although several studies have explored this setting, the recommendation performance remains suboptimal, mainly due to two fundamental challenges: the heterogeneity of user behaviors and data sparsity. To address these challenges, we propose BLADE, a framework that enhances multi-behavior modeling while mitigating data sparsity. Specifically, to handle behavior heterogeneity, we introduce a dual item-behavior fusion architecture that incorporates behavior information at both the input and intermediate levels, enabling preference modeling from multiple perspectives. To mitigate data sparsity, we design three behavior-level data augmentation methods that operate directly on behavior sequences rather than core item sequences. These methods generate diverse augmented views while preserving the semantic consistency of item sequences. These augmented views further enhance representation learning and generalization via contrastive learning. Experiments on three real-world datasets demonstrate the effectiveness of our approach.
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