Simulating News Recommendation Ecosystem for Fun and Profit

Understand

Understanding the evolution of online news communities is essential for designing more effective news recommender systems.

• However, due to the lack of appropriate datasets and platforms, the existing literature is limited in understanding the impact of recommender systems on this evolutionary process and the underlying mechanisms, resulting in sub-optimal system designs that may affect long-term utilities.
• In this work, we propose SimuLine, a simulation platform to dissect the evolution of news recommendation ecosystems and present a detailed analysis of the evolutionary process and underlying mechanisms.
• SimuLine first constructs a latent space well reflecting the human behaviors, and then simulates the news recommendation ecosystem via agent-based modeling.