Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders
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 gr...
| Authors | Cedric Waterschoot, Nava Tintarev, Francesco Barile |
|---|---|
| Primary category | cs.CL |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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