Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
This addresses the challenge of improving explainability and scrutability in recommender systems for users interacting through modern dialog interfaces, though it is incremental as it builds on existing LLM prompting paradigms.
The study tackled the problem of making recommendations from language-based preferences in near cold-start scenarios, finding that large language models (LLMs) provide competitive performance compared to state-of-the-art item-based collaborative filtering methods, with results showing effectiveness in zero-shot or few-shot settings without supervised training.
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.