Simulating News Recommendation Ecosystem for Fun and Profit
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Abstract (arXiv)
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. Based on extensive simulation experiments and the comprehensive analysis framework consisting of quantitative metrics, visualization, and textual explanations, we analyze the characteristics of each evolutionary phase from the perspective of life-cycle theory, and propose a relationship graph illustrating the key factors and affecting mechanisms. Furthermore, we explore the impacts of recommender system designing strategies, including the utilization of cold-start news, breaking news, and promotion, on the evolutionary process, which shed new light on the design of recommender systems.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 54 references that are in the map. First posted on arXiv 23 May 2023.
Builds on
20 of 54 references.
References · 20
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8 more
- 2021Personalized News Recommendation: Methods and Challengescited by 30
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Citing papers per year: 2023: 1.
1 paper.