title: Leveraging Large Language Models in Conversational Recommender Systems
summary: 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.
paper_authors._id: 6462ed0d8bd7025ebe856d6d
paper_authors.name: Luke Friedman
paper_authors._id: 6462ed0d8bd7025ebe856d6e
paper_authors.name: Sameer Ahuja
paper_authors._id: 6462ed0d8bd7025ebe856d6f
paper_authors.name: David Allen
paper_authors._id: 6462ed0d8bd7025ebe856d70
paper_authors.name: Terry Tan
paper_authors._id: 6462ed0d8bd7025ebe856d71
paper_authors.name: Hakim Sidahmed
paper_authors._id: 6462ed0d8bd7025ebe856d72
paper_authors.name: Changbo Long
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T14:58:22.258Z
paper_authors._id: 63eabd0991a1b8ec4fb94a8f
paper_authors.avatarUrl: /avatars/2d823d3aac5c5f1c442068551453ac71.svg
paper_authors.fullname: Changbo Long
paper_authors.type: user
paper_authors.user: longchb
paper_authors._id: 6462ed0d8bd7025ebe856d73
paper_authors.name: Jun Xie
paper_authors._id: 6462ed0d8bd7025ebe856d74
paper_authors.name: Gabriel Schubiner
paper_authors._id: 6462ed0d8bd7025ebe856d75
paper_authors.name: Ajay Patel
paper_authors._id: 6462ed0d8bd7025ebe856d76
paper_authors.name: Harsh Lara
paper_authors._id: 6462ed0d8bd7025ebe856d77
paper_authors.name: Brian Chu
paper_authors._id: 6462ed0d8bd7025ebe856d78
paper_authors.name: Zexi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T14:58:49.358Z
paper_authors._id: 6422a1e33f7db1300acddafc
paper_authors.avatarUrl: /avatars/8cfef829ac3482a3a91c3a536ed76993.svg
paper_authors.fullname: chenzexin
paper_authors.type: user
paper_authors.user: xiaojin666
paper_authors._id: 6462ed0d8bd7025ebe856d79
paper_authors.name: Manoj Tiwari
paper_title: Leveraging Large Language Models in Conversational Recommender Systems
paper_summary: 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.
paper_ai_summary: An end-to-end large-scale conversational recommender system using LLMs addresses challenges in understanding dialogue, retrieving information, and generating synthetic conversations, demonstrating improved personalization and functionality in video recommendations.
paper_ai_keywords: Large Language Models Conversational Recommender System user preference understanding dialogue management explainable recommendations natural language user profiles session-level context user simulator synthetic conversations RecLLM LaMDA