Traditional recommender systems leverage users' item preference history to
recommend novel content that users may like. However, modern dialog interfaces
that allow users to express language-based preferences offer a fundamentally
different modality for preference input. Inspired by recent successes of
prompting paradigms for large language models (LLMs), we study their use for
making recommendations from both item-based and language-based preferences in
comparison to state-of-the-art item-based collaborative filtering (CF) methods.
To support this investigation, we collect a new dataset consisting of both
item-based and language-based preferences elicited from users along with their
ratings on a variety of (biased) recommended items and (unbiased) random items.
Among numerous experimental results, we find that LLMs provide competitive
recommendation performance for pure language-based preferences (no item
preferences) in the near cold-start case in comparison to item-based CF
methods, despite having no supervised training for this specific task
(zero-shot) or only a few labels (few-shot). This is particularly promising as
language-based preference representations are more explainable and scrutable
than item-based or vector-based representations.

传统的推荐系统利用用户对项目的偏好历史记录来推荐新内容，而现代对话界面则提供了一种基于语言偏好表达的基本不同模式。本文研究了大型语言模型 (LLM) 的提示范式在使用基于项目和语言偏好的推荐中与最先进的基于项目的协同过滤 (CF) 方法相比的应用。通过收集既包含基于项目偏好又包含基于语言偏好的新数据集，以及用户对多种（有偏见的）推荐项目和（无偏见的）随机项目的评分，支持了这一调查。在众多实验结果中，我们发现对于纯语言偏好（无项目偏好）的情况，LLM 在近乎冷启动的情况下与基于项目的 CF 方法相比提供了有竞争力的推荐性能，尽管其在这个特定任务上没有进行监督训练（零射击）或只有少数标签（少射击）。这一点尤其令人鼓舞，因为基于语言偏好的表示比基于项目或基于向量的表示更加可解释和可审查。