发表机构
Maastricht University（马斯特里赫特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究群体推荐系统中LLMs能否模拟对群体内偏好分布的敏感性并选理想聚合策略。通过微调LLMs、开发模拟模型生成推荐候选并动态选择，经用户研究验证，该方法在满意度和共识方面表现优，凸显LLMs用于适应性调整的优势。
AI 中文摘要
先前的群体推荐系统研究表明，对群体内偏好分布敏感，偏好聚合策略的选择受益于考虑此类群体配置。本文研究大语言模型（LLMs）是否能模拟这种敏感性，并根据对公平、满意度和共识的细微人类感知选择理想的聚合策略及相应推荐。通过在人类调查数据上微调LLMs作为推荐管道中的实时判断模型，利用从DeepSeek-V3.1提炼的推理数据集和人类真实评估，开发了Judgmental Llama和Judgmental OLMo来模拟群体评估。该管道基于社会选择聚合策略成功生成多个推荐候选，并动态选择使预测的类人评估最大化的那个。在用户研究（n = 284）中验证了这些建议，发现该方法在满意度和群体共识方面得分最高。还发现当考虑基于LLM的方法与群体配置（如少数群体或联盟）之间的交互效应时，LLM判断与人类对公平、满意度和共识的感知最一致。这些发现进一步支持根据特定群体内偏好分布动态调整聚合策略，并突出使用LLMs进行与人类主观判断一致的适应性调整的优势。
英文摘要
Previous work in group recommender systems has demonstrated a sensitivity to the distribution of preferences within a group. Specifically, the selection of the preference aggregation strategy benefits from considering such group configurations. In this paper, we study whether LLMs are able to mimic this sensitivity and to select the ideal aggregation strategy (and corresponding recommendation) according to nuanced human perceptions of fairness, satisfaction, and consensus. We do this by fine-tuning Large Language Models (LLMs) on human survey data to serve as real-time judgmental models within the recommendation pipeline. Using a reasoning dataset distilled from DeepSeek-V3.1 and human ground truth assessments, we develop Judgmental Llama and Judgmental OLMo to simulate group assessments. Our pipeline successfully generates multiple recommendation candidates based on social choice-based aggregation strategies and dynamically selects the one that maximizes these predicted human-like evaluations. We further validate these suggestions in a user study (n=284) and find that our methodology achieved the highest scores for satisfaction and group consensus. Furthermore, we find that LLM judgments are most aligned with human perceptions of fairness, satisfaction and consensus when we also consider interaction effects between our LLM-based method and group configuration (e.g., minority or coalition). These findings give further support for dynamically adapting aggregation strategies to specific within-group preference distributions, and highlight the advantage of using LLMs for an adaptation that is aligned with subjective human judgments.
CommentsFull paper accepted at the 20th ACM Conference on Recommender Systems (RecSys 2026)
Journal refCedric Waterschoot, Nava Tintarev, and Francesco Barile. 2026. Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders. In Proceedings of the 20th ACM Conference on Recommender Systems (RecSys 2026)
