When Algorithms Mirror Minds: A Confirmation-Aware Social Dynamic Model of Echo Chamber and Homogenization Traps
Ming Tang, Xiaowen Huang, Jitao Sang

TL;DR
This paper introduces a confirmation-aware social dynamic model to analyze how recommender systems foster echo chambers and user homogenization, providing theoretical proofs, empirical simulations, and mitigation strategies to address these systemic issues.
Contribution
It presents a novel social dynamic model that incorporates user psychology and social relationships, offering new insights into the emergence of echo chambers and homogenization in recommender systems.
Findings
Echo chambers and homogenization are inevitable under certain conditions.
Mitigation strategies can reduce these effects with some loss in recommendation accuracy.
The model's analysis aligns with empirical data from real-world datasets.
Abstract
Recommender systems increasingly suffer from echo chambers and user homogenization, systemic distortions arising from the dynamic interplay between algorithmic recommendations and human behavior. While prior work has studied these phenomena through the lens of algorithmic bias or social network structure, we argue that the psychological mechanisms of users and the closed-loop interaction between users and recommenders are critical yet understudied drivers of these emergent effects. To bridge this gap, we propose the Confirmation-Aware Social Dynamic Model which incorporates user psychology and social relationships to simulate the actual user and recommender interaction process. Our theoretical analysis proves that echo chambers and homogenization traps, defined respectively as reduced recommendation diversity and homogenized user representations, will inevitably occur. We also conduct…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
