Large Language Models are Competitive Near Cold-start Recommenders for
  Language- and Item-based Preferences
Scott Sanner Krisztian Balog Filip Radlinski Ben Wedin Lucas Dixon
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.