English(EN) CoSimRec: Measuring Coordinated-Content Penetration in Recommender Feedback Loops

新框架CoSimRec衡量推荐系统中协同内容放大

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-16 15:24

研究人员开发了CoSimRec，一个新开发的基于代理的框架，旨在评估推荐系统如何放大协同内容。该框架模拟了反馈循环中的动态排名、用户响应和干预措施，解决了现有静态评估的局限性。CoSimRec引入了算法渗透率（APR）指标来量化目标内容的曝光度和参与度，特别是在协同活动方面。在MIND、MovieLens和LastFM等数据集上的实验表明，基于流行度和反馈敏感的推荐器显著提高了内容渗透率，而同步感知防御可以降低它。 AI

影响提供了一种理解和潜在缓解AI驱动的内容推荐系统内部操纵的新方法。

排序理由学术论文，介绍了一个用于评估推荐系统的新框架和指标。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，介绍了一个用于评估推荐系统的新框架和指标。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 84 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jiuyang Lyu · 2026-07-16 15:24

CoSimRec：衡量推荐器反馈循环中的协同内容渗透

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…

本文如何被排名

报道来源 [1]

CoSimRec：衡量推荐器反馈循环中的协同内容渗透

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