Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks. However, traditional recommender systems continue to face great challenges such as poor interactivity and explainability, which actually also hinder their broad deployment in real-world systems. To address these limitations, this paper proposes a novel paradigm called Chat-Rec (ChatGPT Augmented Recommender System) that innovatively augments LLMs for building conversational recommender systems by converting user profiles and historical interactions into prompts. Chat-Rec is demonstrated to be effective in learning user preferences and establishing connections between users and products through in-context learning, which also makes the recommendation process more interactive and explainable. What's more, within the Chat-Rec framework, user's preferences can transfer to different products for cross-domain recommendations, and prompt-based injection of information into LLMs can also handle the cold-start scenarios with new items. In our experiments, Chat-Rec effectively improve the results of top-k recommendations and performs better in zero-shot rating prediction task. Chat-Rec offers a novel approach to improving recommender systems and presents new practical scenarios for the implementation of AIGC (AI generated content) in recommender system studies.

翻译：大型语言模型（LLMs）已展现出应用于各类任务场景的巨大潜力。然而，传统推荐系统仍面临交互性差和可解释性不足等重大挑战，这些问题实际也阻碍了其在现实系统中的广泛部署。为突破上述局限，本文提出一种名为Chat-Rec（ChatGPT增强推荐系统）的创新范式，通过将用户画像与历史交互转化为提示（prompts），创新性地增强LLMs以构建对话式推荐系统。实验证明，Chat-Rec能通过上下文学习有效学习用户偏好并建立用户与产品间的关联，同时使推荐过程更具交互性与可解释性。此外，在Chat-Rec框架下，用户偏好可跨域迁移至不同产品实现跨域推荐，基于提示的信息注入机制也能处理新物品的冷启动场景。在实验中，Chat-Rec有效提升了Top-K推荐效果，在零样本评分预测任务中表现更优。Chat-Rec为改进推荐系统提供了新思路，并为AIGC（人工智能生成内容）在推荐系统研究中的落地创造了新的实践场景。