Large Language Models are Competitive Near Cold-start Recommenders for
  Language- and Item-based Preferences
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.
64c1c5de6489a1a4cf8bbe39
Scott Sanner
claimed_verified
2023-07-28T08:10:37.090Z
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/avatars/04626ea06177b309391db1171aa97d50.svg
Scott Sanner
user
ssanner
64c1c5de6489a1a4cf8bbe3a
Krisztian Balog
64c1c5de6489a1a4cf8bbe3b
Filip Radlinski
64c1c5de6489a1a4cf8bbe3c
Ben Wedin
64c1c5de6489a1a4cf8bbe3d
Lucas Dixon
claimed_verified
2023-07-27T12:28:52.649Z
64c25a9c2b1ffc2eecbce31d
https://cdn-avatars.huggingface.co/v1/production/uploads/64c25a9c2b1ffc2eecbce31d/uTp3yOPdmCSAmhzWwKUjK.png
Lucas Dixon
user
iislucas
Large Language Models are Competitive Near Cold-start Recommenders for
  Language- and Item-based Preferences
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.
LLMs demonstrate competitive recommendation performance for language-based preferences, even in zero-shot and few-shot scenarios, compared to item-based collaborative filtering methods.
large language models prompting paradigms item-based collaborative filtering cold-start case zero-shot few-shot