The research on intent-enhanced sequential recommendation algorithms focuses on how to better mine dynamic user intent based on user behavior data for sequential recommendation tasks. Various data augmentation methods are widely applied in current sequential recommendation algorithms, effectively enhancing the ability to capture user intent. However, these widely used data augmentation methods often rely on a large amount of random sampling, which can introduce excessive noise into the training data, blur user intent, and thus negatively affect recommendation performance. Additionally, these methods have limited approaches to utilizing augmented data, failing to fully leverage the augmented samples. We propose an intent-enhanced data augmentation method for sequential recommendation(\textbf{IESRec}), which constructs positive and negative samples based on user behavior sequences through intent-segment insertion. On one hand, the generated positive samples are mixed with the original training data, and they are trained together to improve recommendation performance. On the other hand, the generated positive and negative samples are used to build a contrastive loss function, enhancing recommendation performance through self-supervised training. Finally, the main recommendation task is jointly trained with the contrastive learning loss minimization task. Experiments on three real-world datasets validate the effectiveness of our IESRec model.

翻译：意图增强的序列推荐算法研究聚焦于如何基于用户行为数据更好地挖掘动态用户意图以完成序列推荐任务。各类数据增强方法在当前序列推荐算法中得到广泛应用，有效提升了捕捉用户意图的能力。然而，这些广泛采用的数据增强方法往往依赖大量随机采样，可能给训练数据引入过多噪声，模糊用户意图，从而对推荐性能产生负面影响。此外，现有方法对增强数据的利用方式较为有限，未能充分发挥增强样本的价值。本文提出一种面向序列推荐的意图增强数据增强方法（\textbf{IESRec}），该方法通过意图片段插入技术，基于用户行为序列构建正负样本。一方面，生成的正样本与原始训练数据混合进行协同训练以提升推荐性能；另一方面，利用生成的正负样本构建对比损失函数，通过自监督训练增强推荐性能。最终，将主推荐任务与对比学习损失最小化任务进行联合训练。在三个真实数据集上的实验验证了我们提出的IESRec模型的有效性。