Mitigating Exposure Biases in Personalized Timelines through Agent-based Models

Recommender systems are ubiquitous in online social networks. Studying how these systems expose people to information at scale is difficult to do as one cannot assume each user is subject to the same feed condition and building evaluation infrastructure is costly. We present an agent-based model comparing personalization algorithms in how they skew users' network perception, and we demonstrate that a greedy algorithm based on network properties is effective at creating less biased feeds. This underscores the influence that network structure has in determining the effectiveness of recommender systems and offers a tool for mitigating perception biases through algorithmic feed construction.