发表机构
University of Windsor; University of Toronto（温莎大学; 多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文将团队推荐问题转化为专家协作图的端到端链接预测，利用多跳协作信息，在两个大规模数据集上取得最优性能，提出了一种集成的图神经网络团队推荐方法。
AI 中文摘要
团队推荐旨在为给定的一组所需技能选择最优的专家子集，该子集应能形成几乎必然成功的协作团队。现有最优方法是神经多标签分类器，它将技能的稠密向量表示转换为代表最优专家子集的稀疏出现向量。然而，这类方法忽略了专家协作图中编码的专家关系与结构信息，因此无法捕捉团队内专家及其关联技能间的复杂相互依赖关系。此外，技能的稠密向量是预训练得到的，与底层神经分类器相互独立，这阻碍了端到端优化。本文中，我们提出将团队推荐问题重新表述为专家协作图中的端到端链接预测，以利用专家间的多跳团队内和跨团队协作，同时避免两阶段分离训练的不必要复杂性。我们在两个来自不同领域、团队技能分布各异的大规模数据集上开展实验，结果表明该端到端方法具有优越性，并建立了新的最优性能水平。我们的代码可在该 https URL 获取。
英文摘要
Team recommendation aims to select an optimal subset of experts who can form an almost surely successful collaborative team for a given set of required skills. State-of-the-art methods are neural multi-label classifiers that transfer dense vector representations of skills into a sparse occurrence vector representing the optimal subset of experts. Such methods, however, overlook experts' relational and structural information encoded in the expert collaboration graph and, thus, fall short of capturing complex inter-dependencies among experts and their associated skills within teams. Moreover, the skills' dense vectors are pretrained disjointly and independently of the underlying neural classifier, hence, preventing end-to-end optimization. In this paper, we propose to reformulate the team recommendation problem into end-to-end link predictions in the expert collaboration graph to consume multi-hop intra-team and cross-team collaborations among experts while eschewing the unnecessary complexities of the disjoint two-phase training procedure. Our experiments on two large-scale datasets from various domains with distinct distributions of skills in teams demonstrate the superiority of the end-to-end approach and establish a new state of the art. Our code is available at https://github.com/fani-lab/OpeNTF.
