# ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls

arXiv: https://arxiv.org/abs/2201.05646 · HTML: https://mlsanity.com/paper/2201.05646/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Fractional Hedonic Games](https://mlsanity.com/paper/1705.10116/) (2017)
- [On Finding Stable and Efficient Solutions for the Team Formation Problem](https://mlsanity.com/paper/1804.00309/) (2018)

## Built on by (most cited first, 3 of 3)

- [Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals](https://mlsanity.com/paper/2309.09404/) (2023)
- [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)

We introduce an emerging AI-based approach and prototype system for assisting team formation when researchers respond to calls for proposals from funding agencies. This is an instance of the general problem of building teams when demand opportunities come periodically and potential members may vary over time. The novelties of our approach are that we: (a) extract technical skills needed about researchers and calls from multiple data sources and normalize them using Natural Language Processing (NLP) techniques, (b) build a prototype solution based on matching and teaming based on constraints, (c) describe initial feedback about system from researchers at a University to deploy, and (d) create and publish a dataset that others can use.