Building teams and promoting collaboration are two very common business activities. An example of these are seen in the TeamingForFunding problem, where research institutions and researchers are interested to identify collaborative opportunities when applying to funding agencies in response to latter's calls for proposals. We describe a novel system to recommend teams using a variety of AI methods, such that (1) each team achieves the highest possible skill coverage that is demanded by the opportunity, and (2) the workload of distributing the opportunities is balanced amongst the candidate members. We address these questions by extracting skills latent in open data of proposal calls (demand) and researcher profiles (supply), normalizing them using taxonomies, and creating efficient algorithms that match demand to supply. We create teams to maximize goodness along a novel metric balancing short- and long-term objectives. We validate the success of our algorithms (1) quantitatively, by evaluating the recommended teams using a goodness score and find that more informed methods lead to recommendations of smaller number of teams but higher goodness, and (2) qualitatively, by conducting a large-scale user study at a college-wide level, and demonstrate that users overall found the tool very useful and relevant. Lastly, we evaluate our system in two diverse settings in US and India (of researchers and proposal calls) to establish generality of our approach, and deploy it at a major US university for routine use.

翻译：构建团队和促进协作是两种非常常见的商业活动。在"面向资助的团队组建"问题中，研究机构和研究人员在响应基金机构项目征集、寻求合作机会时，便体现了此类活动。本文描述了一个新颖的团队推荐系统，该系统运用多种人工智能方法，旨在确保：(1) 每个团队都能达到机会所要求的最高技能覆盖率；(2) 机会分配的负担在候选成员之间保持平衡。我们通过提取项目征集开放数据中的隐含技能需求与研究者档案中的技能供给，利用分类体系对其进行标准化，并构建需求与供给匹配的高效算法来应对这些挑战。我们根据一项平衡短期与长期目标的新颖度量标准来组建团队，以最大化团队效能。我们从两个维度验证了算法的成功性：(1) 定量验证，通过效能评分评估推荐团队，发现信息更充分的方法能够推荐更少但效能更高的团队；(2) 定性验证，通过在全院范围内开展大规模用户研究，表明用户普遍认为该工具非常有用且具有相关性。最后，我们在美国和印度两个不同环境（涉及研究者与项目征集）中评估了系统以验证其泛化能力，并在美国一所主要大学部署该系统以供日常使用。