Dataset Regeneration for Sequential Recommendation

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.

References in corpus (9)

• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
• Global Context Enhanced Graph Neural Networks for Session-based Recommendation
• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
• Cascaded Diffusion Models for High Fidelity Image Generation
• Filter-enhanced MLP is All You Need for Sequential Recommendation
• MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network
• AutoDenoise: Automatic Data Instance Denoising for Recommendations
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
• CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation

Cited by in corpus (3)

• Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learning
• TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation
• RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations