research-article

Dataset Regeneration for Sequential Recommendation

Authors:

Enhong ChenAuthors Info & Claims

KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3954 - 3965

https://doi.org/10.1145/3637528.3671841

Published: 24 August 2024 Publication History

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.

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Cited By

Xie WZhou RWang HShen TChen ESerra ESpezzano F(2024)Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679756(2618-2628)Online publication date: 21-Oct-2024
https://doi.org/10.1145/3627673.3679756

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cover image ACM Conferences

KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2024

6901 pages

ISBN:9798400704901

DOI:10.1145/3637528

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Northeastern University, USA
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CENTAI / Eurecat, Italy

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Cited By

Xie WZhou RWang HShen TChen ESerra ESpezzano F(2024)Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679756(2618-2628)Online publication date: 21-Oct-2024
https://doi.org/10.1145/3627673.3679756

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