NEW! Our paper
that presents a novel system to recommend teams using a variety of AI methods has been accepted to IAAI-AAAI'2024 and received the Deployed Application award (can be enlarged).
×
NEW! Our demo
paper about deploying ULTRA
in two geographical regions of the world has been accepted to CODS-COMAD'2024.
We 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).
We introduce ULTRA
(University
Lead Team
Builder from RFPs and Analysis), an 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 - coming soon]
[2024] Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to
Call
for Proposals
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.
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 (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.
We have observed that personal health can benefit from meals planned over a period,
like a day or 3-days (travel), or a week. So, our idea is to build and evaluate a personalized meal recommender system to help healthy and people with a
health condition, like diabetics, make optimized meals choices over a time period incorporating food
restrictions, regional cuisine preferences, and balancing short- and long-term constraints to promote
individual health and happiness. The Artificial Intelligence (AI) / Machine Learning (ML) aspects of the
solution consist of using novel knowledge graphs and group recommendation
algorithms. We will also perform a fairness evaluation of our algorithms, i.e., ensure that the
recommended meal plans are not only useful to the user but also are not biased (stereotyped) towards any
group of food items or user groups.
Representative Publications
[2024] BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and
Reasoning on Multimodal Recipes Arxiv Preprint [Paper][BibTex]
Cooking domain is a popular use-case to demonstrate decision-support (AI) capabilities
in service of benefits like precision health with tools ranging from information retrieval interfaces to
task-oriented chatbots. The recipes today are handled as textual documents which makes it difficult for
machines to read, reason and handle ambiguity. This demands a need for better representation of the
recipes, overcoming the ambiguity and sparseness that exists in the current textual documents. We
constructed a machine-understandable rich recipe representation (R3),
in the form of plans, from the recipes available in natural language. R3 is infused with additional
knowledge such as information about allergens and images of ingredients, possible failures and tips for
each atomic cooking step.
To show the benefits of R3, we also built TREAT, a tool for recipe
retrieval which uses R3 to perform
multi-modal reasoning on the recipe's content (plan objects - ingredients and cooking tools), food
preparation process (plan actions and time), and media type (image, text). R3 leads to improved
retrieval efficiency and new capabilities that were hither-to not possible in textual
representation.
Representative Publications
[2022] A Rich Recipe Representation as Plan to Support Expressive Multi-Modal Queries
on Recipe Content and Preparation Process
Workshop on Knowledge Engineering for Planning and Scheduling (KEPS), International Conference
on Automated Planning and Scheduling (ICAPS) [Paper][BibTex]
Figure 4: Difference between textual representation and R3 for a single instruction.
[2022] A Multi-Modal Decision Support System with Allergy-Aware Recipe Understanding Powered by a
Plan Representation [Paper][BibTex]
Figure 5: Result of user query of asking recipes containing bacon.
NEW! Our 
We 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). 
We introduce 
