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<p>Dataset recommendation is pivotal for streamlining data selection and accelerating scientific discovery. In this study, we propose the Sparse-Link Dataset Recommendation Model (SLDRM), an explainable framework that maps textual content, authors, and datasets into a unified topic space. Specifically, SLDRM captures the correlations among words, research communities, and dataset usage patterns by linking their respective latent topics. To handle the inherent sparsity of the research landscape, we incorporate a Spike-and-Slab prior. We validate our model using a real-world dataset collected from the PapersWithCode website. Experimental results show that our model not only improves recommendation accuracy but also enhances interpretability. The proposed model provides researchers with an efficient tool for dataset discovery and deepens the understanding of the knowledge production process in scientific networks.</p>

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Trails to Gold-standard data: Harnessing scientific networks for dataset recommendation

  • 期刊论文

  • 发表日期：

    2026年1月1日

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  • Xin Zhou,
  • Yuanchun Jiang,
  • Yang Qian,
  • Yezheng Liu

摘要

Dataset recommendation is pivotal for streamlining data selection and accelerating scientific discovery. In this study, we propose the Sparse-Link Dataset Recommendation Model (SLDRM), an explainable framework that maps textual content, authors, and datasets into a unified topic space. Specifically, SLDRM captures the correlations among words, research communities, and dataset usage patterns by linking their respective latent topics. To handle the inherent sparsity of the research landscape, we incorporate a Spike-and-Slab prior. We validate our model using a real-world dataset collected from the PapersWithCode website. Experimental results show that our model not only improves recommendation accuracy but also enhances interpretability. The proposed model provides researchers with an efficient tool for dataset discovery and deepens the understanding of the knowledge production process in scientific networks.