Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals

TL;DR

This paper introduces an AI-driven system for recommending research teams by matching open data on proposal demands and researcher skills, optimizing for skill coverage and workload balance, validated through quantitative and user studies.

Contribution

The paper presents a novel algorithmic approach for team recommendation that balances skill coverage and workload, validated across multiple real-world settings and deployed for routine use.

Findings

Higher goodness scores with more informed methods

Smaller, more effective recommended teams

Positive user feedback on tool relevance

Abstract

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.