title: Large Language Models are Competitive Near Cold-start Recommenders for
    Language- and Item-based Preferences
summary: 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.
paper_authors._id: 64c1c5de6489a1a4cf8bbe39
paper_authors.name: Scott Sanner
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-28T08:10:37.090Z
paper_authors._id: 64c2e34301d92942b7d9a6df
paper_authors.avatarUrl: /avatars/04626ea06177b309391db1171aa97d50.svg
paper_authors.fullname: Scott Sanner
paper_authors.type: user
paper_authors.user: ssanner
paper_authors._id: 64c1c5de6489a1a4cf8bbe3a
paper_authors.name: Krisztian Balog
paper_authors._id: 64c1c5de6489a1a4cf8bbe3b
paper_authors.name: Filip Radlinski
paper_authors._id: 64c1c5de6489a1a4cf8bbe3c
paper_authors.name: Ben Wedin
paper_authors._id: 64c1c5de6489a1a4cf8bbe3d
paper_authors.name: Lucas Dixon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-27T12:28:52.649Z
paper_authors._id: 64c25a9c2b1ffc2eecbce31d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64c25a9c2b1ffc2eecbce31d/uTp3yOPdmCSAmhzWwKUjK.png
paper_authors.fullname: Lucas Dixon
paper_authors.type: user
paper_authors.user: iislucas
paper_title: Large Language Models are Competitive Near Cold-start Recommenders for
    Language- and Item-based Preferences
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) prompting paradigms collaborative filtering (CF) cold-start case zero-shot few-shot