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 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 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 https://anonymous.4open.science/r/KDD2024-86EA

翻译：序列推荐系统是现代推荐系统的关键组成部分，旨在捕捉用户不断演变的偏好。已有大量研究致力于提升序列推荐系统的能力。这些方法通常遵循以模型为中心的范式，即在固定数据集上开发有效的模型。然而，这种方法往往忽视了数据本身潜在的质量问题和固有缺陷。受以数据为中心的人工智能潜力的驱动，我们提出了一种新颖的以数据为中心的范式，利用一个与模型无关的数据集再生框架DR4SR来开发理想的训练数据集。该框架能够再生出具有卓越跨架构泛化能力的数据集。此外，我们引入了DR4SR+框架，它集成了一个模型感知的数据集个性化器，专门为目标模型定制再生的数据集。为了证明以数据为中心范式的有效性，我们将我们的框架与多种以模型为中心的方法相结合，并在四个广泛采用的数据集上观察到显著的性能提升。此外，我们进行了深入分析，以探索以数据为中心范式的潜力，并提供有价值的见解。代码可在 https://anonymous.4open.science/r/KDD2024-86EA 找到。