  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
