Recommending Groups (Bundles, Sets) With Trust
In contrast to the typical setting of user-item recommendation, we consider scenarios where one wants to suggest sets (or bundles, groups) meeting some objectives, where each set represents a group of items. This setting is characterized by optimizing a combination of short and long term objectives over a time duration. It also offers new research challenges for meeting fairness and trust in AI-based recommender systems.
Project 1: Recommending researchers for a proposal call, balancing short- and long-term individual and organizations goals
Sponsors: South Carolina Research Authority, University of South Carolina, Cisco
[Tool Website] [Demo Video] [Project Page]
[Tool Website] [Demo Video] [Project Page]
Papers
Siva Likitha Valluru, Sai Teja Paladi, Siwen Yan, Biplav Srivastava, Sriraam Natarajan, Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals, Proc. Thirty-Eightth Annual Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI-24), Preprint on Arxiv at: https://arxiv.org/abs/2309.09404, 2024 [Team recommendation]
Siva Likitha Valluru, Michael Widener, Biplav Srivastava and Sugata Gangopadhyay, ULTRA: Exploring Team Recommendations in Two Geographies Using Open Data in Response to Call for Proposals, Proc. CODS-COMAD, 2024 [Demo, Team recommendation]
Biplav Srivastava, Tarmo Koppel, Sai Teja Palladi, Siva Likitha Valluru, Rohit Sharma and Owen Bond, ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls, ICDM Workshop on AI for Nudging and Personalization (WAIN), 2022. [Presentation] [Preprint] [BibTex][Team Recommendation]
Aniket Gupta, Biplav Srivastava, Karan Aggarwal, Sai Teja Paladi, KITE - An Unsupervised, Effective and Inclusive Approach for Textual Content Exploration, 2022. [Text Analysis, Unsupervised Exploration]
Resources:
Additional Tools: Kite (unsupervised content explorer), Domain Content Mapper (to ACM and JEL), Recommender
Code: GitHub
Project 2: Recommending meals for a person, balancing short- and long-term health goals and constraints
Sponsors: Vajra Program (Govt of India), Cisco
Papers
Hem Chandra Joshi, Utkarsh Yadav, Biplav Srivastava, Ram Manohar Singh, Learning About People’s Attitude Towards Food Available in India and Its Implications for Fair AI-based Systems, ICDM Workshop on AI for Nudging and Personalization (WAIN), 2022. [Presentation] [AI Fairness, Food Survey]
Vishal Pallagani, Priyadharsini Ramamurthy, Vedant Khandelwal, Revathy Venkataramanan, Kausik Lakkaraju, Sathyanarayanan N. Aakur, Biplav Srivastava, 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), 2022, Preprint on Arxiv at: https://arxiv.org/abs/2203.17109, 2022. [Plan Representation, Multimodal Reasoning]
Resources: