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- W4385328102 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." @default.
- W4385328102 created "2023-07-28" @default.
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- W4385328102 date "2023-07-26" @default.
- W4385328102 modified "2023-09-25" @default.
- W4385328102 title "Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences" @default.
- W4385328102 doi "https://doi.org/10.48550/arxiv.2307.14225" @default.
- W4385328102 hasPublicationYear "2023" @default.
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