用大模型做推荐系统的世界模型，通过对比排序理解用户偏好。
LLM-as-a-Judge: Toward World Models for Slate Recommendation Systems
- 让大模型通过两两比较推荐列表来学习用户偏好
- 实验证明模型表现与捕捉偏好函数的能力相关
- 适合对推荐系统建模感兴趣的研究者
跨领域的用户偏好建模仍是板式推荐（即推荐有序项目序列）研究中的关键挑战。本文探究大语言模型（LLM）如何通过在板式间进行成对推理，有效充当用户偏好的世界模型。我们在三个不同数据集上的三项任务中对多个LLM进行了实证研究。结果揭示了任务表现与LLM所捕获的偏好函数特性之间的关系，指出了改进方向，并凸显了LLM作为推荐系统世界模型的潜力。
原文摘要 · Abstract (English)
Modeling user preferences across domains remains a key challenge in slate recommendation (i.e. recommending an ordered sequence of items) research. We investigate how Large Language Models (LLM) can effectively act as world models of user preferences through pairwise reasoning over slates. We conduct an empirical study involving several LLMs on three tasks spanning different datasets. Our results reveal relationships between task performance and properties of the preference function captured by LLMs, hinting towards areas for improvement and highlighting the potential of LLMs as world models in recommender systems.
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