# Leveraging Large Language Models in Conversational Recommender Systems

**Authors:** Luke Friedman, Sameer Ahuja, David Allen, Terry Tan, Hakim Sidahmed, Changbo Long, Jun Xie, Gabriel Schubiner, Ajay Patel, Harsh Lara, Brian Chu, Zexi Chen, Manoj Tiwari  
**arXiv:** [2305.07961](https://arxiv.org/abs/2305.07961)  
**Date:** 2023-05-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.07961v1/

## Key points

- Proposed end-to-end architecture for a Conversational Recommender System (CRS) leveraging Large Language Models (LLMs)
- Real-time multi-turn dialogues incorporating world knowledge into language understanding
- Technical challenges of integrating LLMs into CRS addressed through innovative implementations for user preference understanding, flexible dialogue management, and explainable recommendations
- Utilization of interpretable natural language user profiles for personalized recommendations based on session-level context
- Introduction of techniques for building a controllable LLM-based user simulator to overcome limitations in conversational data availability
- Key component: LLM ranker module generating scores and explanations for candidate items based on metadata like video titles, knowledge graph entities, developer descriptions, transcripts, and user comments
- Use of chain-of-thought reasoning by LLM ranker to score items and provide explanations for recommendation decisions
- Future work includes human evaluations and dataset releases for quantitative evaluation of design alternatives and plans to extend system capabilities beyond conversation interactions

## Summary

This paper proposes an end-to-end architecture for a Conversational Recommender System (CRS) that leverages the power of Large Language Models (LLMs) to enhance user engagement and transparency. The system allows for real-time multi-turn dialogues, incorporating world knowledge into language understanding. However, integrating LLMs into a CRS presents technical challenges such as managing complex conversations and retrieving information from external sources. To address these challenges, the proposed architecture includes innovative implementations for user preference understanding, flexible dialogue management, and explainable recommendations. By utilizing interpretable natural language user profiles, the system can personalize recommendations based on session-level context. Techniques for building a controllable LLM-based user simulator are also introduced to overcome limitations in conversational data availability. A key component of the system is the LLM ranker module which generates scores and explanations for candidate items based on metadata associated with each item. This involves text summarization using metadata such as video titles, knowledge graph entities, developer descriptions, transcripts, and user comments. The LLM ranker employs chain-of-thought reasoning to score items and provide explanations for recommendation decisions. Future work includes human evaluations and dataset releases for quantitative evaluation of design alternatives. Plans to extend the system's capabilities to handle feedback from various channels beyond conversation interactions are also highlighted. Overall, this paper outlines a comprehensive approach to building a large-scale CRS that harnesses the power of LLMs for personalized and engaging recommendations in real-time dialogues.