Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences

• URL: http://arxiv.org/abs/2307.14225v1
• Date: Wed, 26 Jul 2023 14:47:15 GMT
• Title: Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
• Authors: Scott Sanner and Krisztian Balog and Filip Radlinski and Ben Wedin and Lucas Dixon
• Abstract summary: 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.
• Score: 33.81337282939615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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