Exploring Recommender Systems through Agent Driven Text-Based Social Media Simulation

In this project, we establish a text-based social media network comprising of 100-200 AI agents. These agents are tasked with emulating various diverse users that are able to view and interact with other posts by liking, commenting, or following the user. These user-to-post interactions are visualized in a graph based database, allowing us to observe the evolution of user preferences over time and how the recommender system impacts overall user behavior.

Interns: Natalie Berry, Soham Harkare, Radha Joshi, Clara Luo, Ami Mistry

Mentors: Quinn Mood (AOS/QPR), Ananya Patri (AMDS/A3D), Sam Scheck (REDD/RQC)