Date of Award
Spring 2025
Document Type
Open Access Dissertation
Department
Computer Science and Engineering
First Advisor
Biplav Srivastava
Abstract
On the internet, where the number of available choices for information is exponentially growing, there is a need to prioritize and deliver relevant results to users efficiently, on demand. Recommendation systems (RSs) address that need by searching through and filtering large amounts of dynamically generated information and providing users with recommendations tailored to them. These systems have primarily focused on (1) single-user models, where recommendations are tailored towards a specific individual, or (2) single-item models, where items are recommended based on a broader appeal to users and similarities in item metadata, in the past. In many real-world scenarios, however, recommendation systems are often tailored to groups of users rather than individual users. This shift from single users and towards groups brings forth unique challenges, such as addressing the diverse needs of a group, resolving conflicts and reaching consensus, assuring that groups receive fair recommendations without bias towards protected characteristics such as race, gender, and socioeconomic status; and ensuring that recommendations remain stable and adaptable even in response to dynamic and evolving environments. Such systems are growing increasingly relevant in disciplines such as team management, healthcare, online communities, and entertainment. This dissertation explores and addresses the unique challenges of group recommendation systems (GRSs), such as increased computational complexity, varying group dynamics, and principles of trustworthy artificial intelligence (AI) related to bias, fairness, and robustness. The research contributes developing novel methods and metrics for GRSs, evaluating them with respect to performance through real-world case studies, such as team formation (project management) and meal planning (healthcare), and improving fairness and robustness of group recommendations. For team recommendation, a prototype system, called ULTRA (University-Lead Team Builder from Requests for Proposals and Analysis, implements the novel methods. It is then evaluated to demonstrate its effectiveness and generality in the context of research funding at academic institutions in the United States (US) and India. This work releases comprehensive datasets to aid in developing adaptable and reliable GRSs that optimize both performance and user satisfaction in dynamic environments. Beyond research, the dissertation is opening new avenues in entrepreneurship, intellectual property, and teaching resources. Keywords: Group Recommendation, Group Recommender Systems (GRSs), Team Formation, Artificial Intelligence (AI), Boosted Bandit Learning, Ensembling, Ensemble Methods, Large Language Models (LLMs), Trusted AI, Fairness, Robustness.
Rights
© 2025, Siva Likitha Valluru
Recommended Citation
Valluru, S.(2025). Building Trustable Methods for Group Recommendations: Advancing Fairness and Robustness Across Domains. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/8291