  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.
