CoSimRec: Measuring Coordinated-Content Penetration in Recommender Feedback Loops
Information RetrievalSocial and Information Networks
Summary
The authors created a new way to test how coordinated groups of accounts can influence what content gets recommended to real users. Their system, CoSimRec, simulates how these groups, recommendation algorithms, and real users interact over time. They introduced a new metric called the Algorithmic Penetration Rate (APR) to measure how much coordinated content reaches and engages real users compared to no interference. Testing on several datasets, they found that popularity-based and feedback-aware recommenders are more vulnerable to coordinated influence, while special ranking methods can reduce this effect.
Authors
Nan Li, Jiahong Shao, Jiuyang Lyu
Abstract
Recommender systems increasingly shape which content reaches users, making it important to understand whether coordinated activity is amplified beyond the accounts that initiate it. Existing robustness evaluations largely focus on static target-rank changes and do not capture how coordinated interactions, recommendation, and user response evolve within a feedback loop. To address this gap, we propose CoSimRec, an offline agent-based evaluation framework that models coordinated accounts, dynamic ranking, non-bot responses, and ranking interventions in a shared closed-loop process. CoSimRec introduces the Algorithmic Penetration Rate (APR) metric family to measure target content's share of non-bot exposure and engagement, lift against matched no-attack baselines, and exposure gained per coordinated interaction. We evaluate CoSimRec on MIND, MovieLens, and LastFM using random, popularity-based, feedback-sensitive, MF, and BPR-MF recommenders, with ten-seed inference for the primary APR analysis and population-scale experiments of up to 1000 users. Random controls show no statistically supported positive penetration, whereas popularity-based and feedback-sensitive ranking produce significant positive APR-Lift in all six master-worker dataset--recommender settings, reaching 0.4505 on LastFM; synchronization-aware ranking reduces APR in every corresponding defense setting.