The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users. Significant efforts have been made to enhance the capabilities of SR systems. These methods typically follow the \textbf{model-centric} paradigm, which involves developing effective models based on fixed datasets. However, this approach often overlooks potential quality issues and flaws inherent in the data. Driven by the potential of \textbf{data-centric} AI, we propose a novel data-centric paradigm for developing an ideal training dataset using a model-agnostic dataset regeneration framework called DR4SR. This framework enables the regeneration of a dataset with exceptional cross-architecture generalizability. Additionally, we introduce the DR4SR+ framework, which incorporates a model-aware dataset personalizer to tailor the regenerated dataset specifically for a target model. To demonstrate the effectiveness of the data-centric paradigm, we integrate our framework with various model-centric methods and observe significant performance improvements across four widely adopted datasets. Furthermore, we conduct in-depth analyses to explore the potential of the data-centric paradigm and provide valuable insights. The code can be found at \textcolor{blue}{\url{https://anonymous.4open.science/r/KDD2024-86EA/}}

翻译：序列推荐系统作为现代推荐系统的核心组件，旨在捕捉用户动态演化的偏好。现有研究主要通过**模型中心**范式，基于固定数据集开发高效模型以提升系统性能。然而，该方法往往忽视了数据本身可能存在的质量缺陷。受**数据中心**人工智能理念的启发，我们提出一种新颖的数据中心范式，通过模型无关的数据集再生框架DR4SR构建理想的训练数据集。该框架能够生成具备卓越跨架构泛化能力的数据集。此外，我们进一步提出DR4SR+框架，通过集成模型感知的数据集个性化模块，为目标模型定制再生数据集。为验证数据中心范式的有效性，我们将所提框架与多种模型中心方法相结合，在四个广泛使用的数据集上均观测到显著的性能提升。通过深入分析，我们进一步探索了数据中心范式的潜力并提供了有价值的见解。代码已公开于 \textcolor{blue}{\url{https://anonymous.4open.science/r/KDD2024-86EA/}}。