Researchers have developed CoSimRec, a new agent-based framework designed to evaluate how recommender systems amplify coordinated content. This framework models dynamic ranking, user responses, and interventions within a feedback loop, addressing limitations of existing static evaluations. CoSimRec introduces the Algorithmic Penetration Rate (APR) metric to quantify the exposure and engagement of target content, particularly in relation to coordinated activity. Experiments on datasets like MIND, MovieLens, and LastFM demonstrated that popularity-based and feedback-sensitive recommenders significantly increase content penetration, while synchronization-aware defenses can reduce it. AI
IMPACT Provides a new method for understanding and potentially mitigating manipulation within AI-driven content recommendation systems.
RANK_REASON Academic paper introducing a new framework and metric for evaluating recommender systems. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.IR (Information Retrieval) →
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