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.

翻译：跨领域建模用户偏好仍是板岩推荐（即推荐有序项目序列）研究中的关键挑战。本研究探讨大型语言模型如何通过板岩间的成对推理，有效充当用户偏好的世界模型。我们在涵盖不同数据集的三个任务上对多种LLM进行了实证研究。结果表明，任务性能与LLM捕获的偏好函数特性之间存在关联，这为改进方向提供了线索，并凸显了LLM作为推荐系统中世界模型的潜力。