AI4Society Home  / 

Trustworthy Group Recommendation

Key Idea

Our research in Trustworthy Group Recommendation focuses on developing fair, transparent, robust, and user-aligned algorithms that support effective group decision-making, with applications like ULTRA that quickly help form research teams and match them with relevant funding opportunities.


Quick Start

  1. See our latest work on BEACON [demo], a personalized meal planning system to assist with making optimized meal choices over a time period, incorporating various food restrictions, chronic health conditions, regional cuisine preferences, as well as balancing short- and long-term constraints to promote individual health and happines [Paper] [Github - capstone prototype]
  2. 🎉 2024 AAAI-IAAI Deployed Application Award for our AI application, <i>Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals</i> 🎉 See our past work on ULTRA, a novel AI-based system for assisting team formation when researchers respond to RFPs from funding agencies. It has been awarded the Deployed Application Award by AAAI-IAAI 2024 (can be enlarged on the right) and featured in AI Magazine 2024: [Paper] [Demo Website]
Group Recommendation and Fairness

Key Contacts: Biplav Srivastava, Siva Likitha Valluru, Michael Huhns, Sriraam Natarajan

Group Recommendation - Research Potential, Methods, and FairnessWe study the problem of group recommendation, an information exploration paradigm that retrieves interesting items for users based on their profiles and past interactions/activities/history. Existing literature encourages using greedy methods, genetic and heuristic algorithms, topic diversification, and cost constraint bi-objective optimizations. Our objective is to build novel methods and useful tools for group recommendation with fairness, and drive different use cases (e.g., meal recommendation).

The underlying research directions and applications are summarized in the poster to the right (can be enlarged).

Team Formation

Technical Lead: Biplav Srivastava
Collaborators over the years: Siva Likitha Valluru, Sai Teja Paladi, Michael Widener, Rohit Sharma, Owen Bond, Ronak Shah, Austin Hetherington
External Collaborators: Aniket Gupta, Siwen Yan, Sriraam Natarajan, Tarmo Koppel, Sugata Gangopadhyay
Advisors: Michael Matthews, Paul Ziehl, Michael Huhns, Danielle McElwain

A poster of ULTRA.We introduce ULTRA (University Lead Team Builder from RFPs and Analysis), a novel AI-based system for assisting team formation when researchers respond to RFPs 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 open data sources and normalize them using NLP techniques, (b) build teaming solutions based on constraints, (c) computationally and qualitatively evaluate our system in two diverse settings (US, India) to establish generality of our approach, and (d) create and publish a dataset that others can use.

(This research study has been certified as exempt from the IRB per 45 CFR 46.104(d)(3) and 45 CFR 46.111(a)(7) by University of South Carolina IRB#Pro00127449.)

Representative Publications

  • [2024] AI-Assisted Research Collaboration with Open Data for Fair and Effective Response to Call for Proposals
    AAAI AI Magazine
    [Paper]

  • 🎉 2024 AAAI-IAAI Deployed Application Award for our AI application, <i>Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals</i> 🎉[2024] Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals [Awarded Deployed Application Award from AAAI-IAAI 2024]
    The Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24)
    [Tool Websites at UofSC, IIT-R] [Demo Videos for UofSC, IIT-R] [Paper] [BibTex]


  • [2024] ULTRA: Exploring Team Recommendations in Two Geographies Using Open Data in Response to Call for Proposals.
    ACM India Joint International Conference on Data Science and Management of Data (CODS-COMAD-2024)
    [Paper] [BibTex]

  • [2022] ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls.
    IEEE ICDM Workshop on AI for Nudging and Personalization (WAIN)
    [Paper] [BibTex]

    • Figure 1: Teaming setup of ULTRA.
    Figure 2: System architecture of ULTRA.

Additional Tools

Collaborators over the years: Aniket Gupta, Biplav Srivastava, Karan Aggarwal, Sai Teja Paladi

Here, we describe some of the important tools that we have developed as part of the ULTRA effort. They started out as useful features that we then made into stand-alone capabilities recognizing their potentia for wider usage:

  • KITE - An Unsupervised, Effective and Inclusive Approach for Textual Content Exploration.KITE (right) is an unsupervised system for exploring textual data which can generate insights from a general as well as a domain-dependent perspective consisting of holistic views, entity-centric view, events view, domain-specific interpretation using industry taxonomies and a detailed full-text view transparently connecting the document to insight elements.

  • We also developed a text-to-classification mapper, a tool that takes the input as a text and matching threshold as a number and returns the ACM or JEL classification codes and description based on the input text.

Representative Publications

  • [2022] KITE - An Unsupervised, Effective and Inclusive Approach for Textual Content Exploration.
    [Tool Website] [Paper] [GitHub] [BibTex]


  • [2022] A Text-to-Classification Mapper (Using ACM/JEL Subject Ontology Codes).
    [Tool Website]

    Figure 3: A demo of text-to-classification mapper.
Meal Recommendation

Our meal recommendation work, including BEACON and related recipe-aware recommendation research, is collected here with the demo, publications, and supporting material.

[View Meal Recommendation]

Exercise Recommendation

This space is for our exercise recommendation work, and we will add project material here as that effort develops.

[View Exercise Recommendation]