A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an unprecedented ability to converse naturally and incorporate world knowledge and common-sense reasoning into language understanding, unlocking the potential of this paradigm. However, effectively leveraging LLMs within a CRS introduces new technical challenges, including properly understanding and controlling a complex conversation and retrieving from external sources of information. These issues are exacerbated by a large, evolving item corpus and a lack of conversational data for training. In this paper, we provide a roadmap for building an end-to-end large-scale CRS using LLMs. In particular, we propose new implementations for user preference understanding, flexible dialogue management and explainable recommendations as part of an integrated architecture powered by LLMs. For improved personalization, we describe how an LLM can consume interpretable natural language user profiles and use them to modulate session-level context. To overcome conversational data limitations in the absence of an existing production CRS, we propose techniques for building a controllable LLM-based user simulator to generate synthetic conversations. As a proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos built on LaMDA, and demonstrate its fluency and diverse functionality through some illustrative example conversations.

翻译：对话推荐系统通过实时多轮对话交互，为用户提供更高的透明度和控制权。近期，大型语言模型展现出前所未有的自然对话能力，能够将世界知识与常识推理融入语言理解，释放了这一范式的潜力。然而，在对话推荐系统中有效利用大型语言模型面临新的技术挑战，包括正确理解与管控复杂对话、从外部信息源检索数据。这些问题因大规模动态变化的项目库以及训练对话数据的匮乏而愈发严峻。本文为使用大型语言模型构建端到端大规模对话推荐系统提供了路线图。具体而言，我们提出了新的实现方案，包括用户偏好理解、灵活对话管理和可解释推荐，作为由大型语言模型驱动的集成架构组成部分。为增强个性化能力，我们描述了如何使大型语言模型解析可解释的自然语言用户画像，并利用其调节会话级上下文。针对现有生产级对话推荐系统缺失导致的对话数据局限问题，我们提出了构建可控大型语言模型用户模拟器以生成合成对话的技术。作为概念验证，我们推出了基于LaMDA构建的YouTube视频大规模对话推荐系统RecLLM，并通过示例对话展示了其流畅性与多样化功能。