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

arXiv: https://arxiv.org/abs/2309.09404 · HTML: https://mlsanity.com/paper/2309.09404/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Managing Popularity Bias in Recommender Systems with Personalized Re-ranking](https://mlsanity.com/paper/1901.07555/) (2019)
- [Fractional Hedonic Games](https://mlsanity.com/paper/1705.10116/) (2017)
- [Team size matters: Collaboration and scientific impact since 1900](https://mlsanity.com/paper/1410.8544/) (2014)
- [Relational Boosted Bandits](https://mlsanity.com/paper/2012.09220/) (2020)
- [ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls](https://mlsanity.com/paper/2201.05646/) (2022)
- [An artificial intelligence tool for heterogeneous team formation in the classroom](https://mlsanity.com/paper/1604.04721/) (2016)

## Built on by (most cited first, 2 of 2)

- [BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes](https://mlsanity.com/paper/2406.13714/) (2024)
- [A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON](https://mlsanity.com/paper/2412.17910/) (2024)

## Abstract (arXiv)

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.