LLM辅助重排序以在推荐系统中实现细微目标
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
AI总结：
本研究通过零样本指令提示对YouTube侧边栏候选进行重排序，发现无约束的LLM辅助重排序会放大极端和阴谋论内容，而轻量级提示正则化可在轻微损失相关性的情况下减少极端内容并增加意识形态多样性。
摘要：
推荐系统已从内容组织工具发展为塑造日常行为的复杂系统。通过控制我们所看到的内容，它们塑造了我们的感知，引发了对过滤气泡、激进化、两极分化和社会不平等的担忧。大型语言模型（LLM）实现了更强大的个性化，加剧了这些动态。然而，大多数推荐系统针对参与度或有限的准确性指标进行调优，很少关注更广泛的社会影响，例如个性化如何重塑社会重要领域中的曝光度。我们研究了LLM辅助重排序在提高个性化的同时，是否无意中放大了对意识形态极端或阴谋论政治内容的曝光，这是一种在新闻推荐中理论上存在但尚未得到实证表征的风险。使用真实的新闻消费历史，我们通过零样本、基于指令的提示对YouTube侧边栏候选进行重排序。我们比较了基线提示与一个约束变体，该变体保持主题相关性并扩大意识形态曝光，同时减少阴谋论或极端内容。在没有约束的情况下，重排序加强了个性化，但增加了对历史中包含此类内容的用户的阴谋论和极端主义材料的曝光。轻量级提示级正则化减少了对极端内容的推广并增加了意识形态多样性，同时相关性损失较小。合成实验表明，LLM通过语言中的统计规律而非对意识形态的语义理解进行重排序，这解释了为什么朴素提示会放大这些模式，而正则化可以重塑它们。总之，我们的结果突显了LLM在高风险推荐中实现上下文细微差别的能力，以及评估LLM辅助个性化超越准确性并将提示设计视为有价值负载而非中性默认的必要性。
英文摘要：
Recommender systems have grown from content-organization tools into sophisticated systems that shape daily behavior. By controlling what we see, they shape what we perceive, raising concerns about filter bubbles, radicalization, polarization, and social inequality. Large language models (LLMs) enable more powerful personalization, intensifying these dynamics. Yet most recommenders are tuned for engagement or limited accuracy metrics, with little attention to broader social implications, e.g. how personalization reshapes exposure in socially consequential domains. We investigate whether LLM-assisted reranking, while improving personalization, inadvertently amplifies exposure to ideologically extreme or conspiratorial political content, a risk theorized but not empirically characterized in news recommendation. Using real news-consumption histories, we rerank YouTube's sidebar candidates through zero-shot, instruction-based prompting. We compare a baseline prompt with a constrained variant that preserves topical relevance and broadens ideological exposure while reducing conspiratorial or extreme content. Without constraints, reranking strengthened personalization but increased exposure to conspiratorial and extremist material for users whose histories contained such content. Lightweight prompt-level regularization reduced promotion of extreme content and increased ideological diversity, with modest relevance loss. Synthetic experiments suggest that LLMs rerank via statistical regularities in language rather than semantic understanding of ideology, clarifying why naive prompts amplify these patterns and why regularization can reshape them. Together, our results highlight the power of LLMs to operationalize contextual nuance in high-stakes recommendation, and the need to evaluate LLM-assisted personalization beyond accuracy and treat prompt design as a value-laden rather than neutral default.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
