Dataset Regeneration for Sequential Recommendation

TL;DR

This paper introduces a data-centric approach for sequential recommendation systems, focusing on regenerating high-quality datasets that improve model performance across multiple architectures and datasets.

Contribution

The paper proposes DR4SR, a model-agnostic dataset regeneration framework, and DR4SR+, a model-aware personalizer, advancing data-centric AI in sequential recommendation.

Findings

Significant performance improvements across four datasets.

Frameworks are compatible with various model-centric methods.

In-depth analysis supports the potential of data-centric paradigm.

Abstract

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://github.com/USTC-StarTeam/DR4SR.