AI 中文总结
研究推荐反馈循环中协同内容渗透问题，提出CoSimRec离线代理评估框架，通过算法渗透率指标家族衡量相关情况，在多数据集和推荐器上评估，发现随机控制无显著正向渗透，基于流行度和反馈敏感排名有提升，同步感知排名降低APR。
AI 中文摘要
推荐系统对用户获取内容影响渐大，了解协同活动是否会在发起账户之外得到放大很重要。现有稳健性评估多关注静态目标排名变化，未捕捉反馈循环中协同交互、推荐及用户响应的演变。为此提出CoSimRec，一个基于离线代理的评估框架，能在共享闭环过程中对协同账户、动态排名、非机器人响应和排名干预进行建模。引入算法渗透率（APR）指标家族来衡量目标内容在非机器人曝光和参与中的占比、相对于匹配无攻击基线的提升以及每次协同交互获得的曝光。在多个数据集和推荐器上进行评估，随机控制无显著正向渗透，基于流行度和反馈敏感的排名产生显著正向APR提升，同步感知排名降低APR。
英文摘要
Recommender systems shape which content reaches users, making it important to measure whether coordinated activity gains visibility 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. We propose CoSimRec, an offline agent-based evaluation framework that models coordinated accounts, dynamic ranking, controlled non-bot responses, and ranking interventions in a shared closed-loop process. CoSimRec introduces the Algorithmic Penetration Rate (APR) metric family: exposure APR is the primary endpoint, while behavior APR is a response-model-conditional sensitivity measure; both can be compared with matched no-attack baselines. We evaluate CoSimRec on MIND, MovieLens, and LastFM with random, popularity-based, feedback-sensitive, MF, BPR-MF, and BPR-LightGCN recommenders. In a risk-blind primary protocol, random controls show no statistically supported positive penetration, whereas popularity-based and feedback-sensitive ranking produce positive APR-Lift in all six master-worker settings, reaching 0.4702 on LastFM. A nine-target MovieLens 1M LightGCN stress test shows positive mean APR-Lift around 25\% injection in all three target-popularity strata, while no-filler profiles remain near zero. Under these controlled conditions, coordinated inputs reach non-bot recommendation slots, providing evidence of a computational pathway from organized activity to audience-level visibility.
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