Sequential recommender systems have demonstrated strong capabilities in modeling users' dynamic preferences and capturing item transition patterns. However, real-world user behaviors are often noisy due to factors such as human errors, uncertainty, and behavioral ambiguity, which can lead to degraded recommendation performance. To address this issue, recent approaches widely adopt self-supervised learning (SSL), particularly contrastive learning, by generating perturbed views of user interaction sequences and maximizing their mutual information to improve model robustness. However, these methods heavily rely on their pre-defined static augmentation strategies~(where the augmentation type remains fixed once chosen) to construct augmented views, leading to two critical challenges: (1) the optimal augmentation type can vary significantly across different scenarios; (2) inappropriate augmentations may even degrade recommendation performance, limiting the effectiveness of SSL. To overcome these limitations, we propose an adaptive augmentation framework. We first unify existing basic augmentation operations into a unified formulation via structured transformation matrices. Building on this, we introduce AsarRec (Adaptive Sequential Augmentation for Robust Sequential Recommendation), which learns to generate transformation matrices by encoding user sequences into probabilistic transition matrices and projecting them into hard semi-doubly stochastic matrices via a differentiable Semi-Sinkhorn algorithm. To ensure that the learned augmentations benefit downstream performance, we jointly optimize three objectives: diversity, semantic invariance, and informativeness. Extensive experiments on three benchmark datasets under varying noise levels validate the effectiveness of AsarRec, demonstrating its superior robustness and consistent improvements.

翻译：序列推荐系统在建模用户动态偏好和捕捉项目转移模式方面展现了强大的能力。然而，现实世界中用户行为常因人为错误、不确定性以及行为模糊等因素而包含噪声，这可能导致推荐性能下降。为解决该问题，近期方法广泛采用自监督学习（尤其是对比学习），通过生成用户交互序列的扰动视图并最大化其互信息来提升模型鲁棒性。然而，这些方法高度依赖其预定义的静态增强策略（即增强类型选定后固定不变）来构建增强视图，从而引发两个关键挑战：（1）最优增强类型可能随不同场景显著变化；（2）不恰当的增强甚至可能降低推荐性能，限制了自监督学习的有效性。为克服这些局限，我们提出一种自适应增强框架。首先，通过结构化变换矩阵将现有基础增强操作统一为统一公式。在此基础上，我们引入AsarRec（面向鲁棒序列推荐的自适应序列增强），该方法通过将用户序列编码为概率转移矩阵，并利用可微分的半-辛克霍恩算法将其投影为硬半双随机矩阵，从而学习生成变换矩阵。为确保学习到的增强有益于下游性能，我们联合优化三个目标：多样性、语义不变性和信息性。在三个涵盖不同噪声水平的基准数据集上进行的大量实验验证了AsarRec的有效性，展示了其优越的鲁棒性和一致的性能提升。