RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation

IF 8.3 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Weibin Liao, Yifan Zhu, Yanyan Li, Qi Zhang, Zhonghong Ou, Xuesong Li
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引用次数: 0

Abstract

Acquiring reviewers for academic submissions is a challenging recommendation scenario. Recent graph learning-driven models have made remarkable progress in the field of recommendation, but their performance in the academic reviewer recommendation task may suffer from a significant false negative issue. This arises from the assumption that unobserved edges represent negative samples. In fact, the mechanism of anonymous review results in inadequate exposure of interactions between reviewers and submissions, leading to a higher number of unobserved interactions compared to those caused by reviewers declining to participate. Therefore, investigating how to better comprehend the negative labeling of unobserved interactions in academic reviewer recommendations is a significant challenge. This study aims to tackle the ambiguous nature of unobserved interactions in academic reviewer recommendations. Specifically, we propose an unsupervised Pseudo Neg-Label strategy to enhance graph contrastive learning (GCL) for recommending reviewers for academic submissions, which we call RevGNN. RevGNN utilizes a two-stage encoder structure that encodes both scientific knowledge and behavior using Pseudo Neg-Label to approximate review preference. Extensive experiments on three real-world datasets demonstrate that RevGNN outperforms all baselines across four metrics. Additionally, detailed further analyses confirm the effectiveness of each component in RevGNN.
RevGNN:用于学术评审人推荐的负采样增强型对比图学习
为学术论文获取审稿人是一个极具挑战性的推荐场景。最近的图学习驱动模型在推荐领域取得了显著进展,但它们在学术审稿人推荐任务中的表现可能存在严重的假否定问题。这是因为假设未观察到的边代表负样本。事实上,匿名评审机制会导致评审人与投稿之间的互动暴露不足,从而导致未观察到的互动数量高于评审人拒绝参与所导致的互动数量。因此,研究如何更好地理解学术审稿人推荐中未观察到的互动的负面标签是一项重大挑战。本研究旨在解决学术审稿人推荐中未观察到的互动的模糊性问题。具体来说,我们提出了一种无监督伪负标策略(Pseudo Neg-Label)来增强图对比学习(GCL),用于为学术论文推荐审稿人,我们称之为 RevGNN。RevGNN 采用两阶段编码器结构,利用伪负标对科学知识和行为进行编码,从而近似地确定审稿偏好。在三个真实世界数据集上进行的广泛实验表明,RevGNN 在四个指标上均优于所有基线。此外,详细的进一步分析证实了 RevGNN 中每个组件的有效性。
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来源期刊
ACS Applied Materials & Interfaces
ACS Applied Materials & Interfaces 工程技术-材料科学:综合
CiteScore
16.00
自引率
6.30%
发文量
4978
审稿时长
1.8 months
期刊介绍: ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.
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