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：组建团队和促进合作是两种非常常见的商业活动。在“为经费而组队”问题中即体现了这一点：研究机构与研究人员在响应资助机构的项目征集时，希望识别合作机会。我们描述了一种新颖的系统，利用多种人工智能方法推荐团队，使得：(1) 每个团队能尽可能全面地覆盖项目所需技能，(2) 项目分配的 workload 在候选成员之间保持平衡。我们通过以下方式解决这些问题：从项目征集（需求）和研究者简介（供给）的开放数据中提取潜在技能，利用分类体系对其进行标准化，并创建高效算法将需求与供给相匹配。我们基于一种平衡短期与长期目标的新型指标来组建团队，以最大化团队质量。我们通过两种方式验证算法的成功：(1) 定量评估，使用质量分数对推荐团队进行评价，发现信息更充分的方法能推荐更少的团队但质量更高；(2) 定性评估，在学院范围内进行大规模用户研究，表明用户普遍认为该工具非常有用且相关性高。最后，我们在美国和印度两个不同的场景（涉及研究者和项目征集）中评估了我们的系统，以证明其通用性，并将其部署于美国一所主要大学供日常使用。