NSF CRII: III: Complementarity Learning for Contextual Behavior Modeling

Project Description (NSF IIS-1849816)

Given the complexity of human behaviors, it is difficult to develop a successful plan and make right decisions. Behavior data in fields such as social media, education, and academic research have been increasingly available for behavioral pattern discovery, decision making, and planning. Complementarity has been revealed of playing a significant role in many fields: partners need complementary strengths to do successful business; courses need complementary teaching materials to achieve effective student learning. Therefore, the representation of human behaviors should preserve the complementarity information rather than the similarity. The purpose of this project isto develop complementarity learning models to advance our understanding of human behaviors in dynamic, social, and spatiotemporal environments, and practically, to facilitate prediction, recommendation, and decision-making and planning processes towards the effectiveness of behaviors. This project will also support educational and outreach programs that will broaden participation in computer science. Open source software implementations of the new algorithms will be made available to the public, and will also serve as an educational tool for junior researchers. Research supervision and career mentoring will be made available to K-12 students through the development and publication, and a new course in data science and behavior modeling will be offered to undergraduate and graduate students.

This project will develop and evaluate novel behavior modeling methods that learn the representation of human behavior by preserving the structure of complementarity among the behavior's components. The idea is that decision makers are looking for not similar but complementary partners, resources, and conditions that provide extra power to make a behavior plan more effective. In this project, principled metrics of complementarity that satisfy intuitive axioms will be proposed; complementarity representation learning methods will be developed, applied, and evaluated on prediction and recommendation tasks. In addition, this project will result in an online recommender system that facilitate young researchers for project teaming and planning. The results will also be disseminated through tutorial and workshop organization at international conferences.

We are grateful for NSF support to make this project possible!


Research Assistants