Sequential recommendation leverages interaction sequences to predict forthcoming user behaviors, crucial for crafting personalized recommendations. However, the true preferences of a user are inherently complex and high-dimensional, while the observed data is merely a simplified and low-dimensional projection of the rich preferences, which often leads to prevalent issues like data sparsity and inaccurate model training. To learn true preferences from the sparse data, most existing works endeavor to introduce some extra information or design some ingenious models. Although they have shown to be effective, extra information usually increases the cost of data collection, and complex models may result in difficulty in deployment. Innovatively, we avoid the use of extra information or alterations to the model; instead, we fill the transformation space between the observed data and the underlying preferences with randomness. Specifically, we propose a novel model-agnostic and highly generic framework for sequential recommendation called sample enrichment via temporary operations on subsequences (SETO), which temporarily and separately enriches the transformation space via sequence enhancement operations with rationality constraints in training. The transformation space not only exists in the process from input samples to preferences but also in preferences to target samples. We highlight our SETO's effectiveness and versatility over multiple representative and state-of-the-art sequential recommendation models (including six single-domain sequential models and two cross-domain sequential models) across multiple real-world datasets (including three single-domain datasets, three cross-domain datasets and a large-scale industry dataset).

翻译：序列推荐利用交互序列预测用户未来行为，这对构建个性化推荐至关重要。然而，用户的真实偏好本质上是复杂且高维的，而观测数据仅是丰富偏好的简化低维投影，这常导致数据稀疏性和模型训练不准确等普遍问题。为从稀疏数据中学习真实偏好，现有研究大多致力于引入额外信息或设计精巧模型。尽管这些方法已被证明有效，但额外信息通常增加数据收集成本，复杂模型则可能导致部署困难。我们创新性地避免了使用额外信息或修改模型，而是通过随机性填充观测数据与潜在偏好之间的转换空间。具体而言，我们提出了一种新颖的模型无关且高度通用的序列推荐框架——基于子序列临时操作的样本增强（SETO），该框架在训练期间通过具有合理性约束的序列增强操作，临时且独立地丰富转换空间。转换空间不仅存在于从输入样本到偏好的过程中，也存在于偏好到目标样本的过程中。我们在多个真实数据集（包括三个单域数据集、三个跨域数据集和一个大规模工业数据集）上，通过多种代表性及最先进的序列推荐模型（包括六个单域序列模型和两个跨域序列模型），验证了SETO的有效性和通用性。