# Leveraging Large Language Models in Conversational Recommender Systems

**Authors:** Luke Friedman, Sameer Ahuja, David Allen, Zhenning 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/2305.07961v2/

## Key points

- Building a Conversational Recommender System (CRS) using Large Language Models (LLMs)
- Challenges in integrating LLMs into CRS such as managing complex conversations and retrieving information from external sources
- Proposed roadmap for developing an end-to-end large-scale CRS powered by LLMs with key components including user preference understanding, flexible dialogue management, and explainable recommendations
- Use of interpretable natural language user profiles for improved personalization at the session level
- Introduction of RecLLM as a large-scale CRS for YouTube videos built on LaMDA showcasing fluency and diverse functionality through example conversations
- Implementation of an LLM ranker module that generates scores and explanations for candidate items based on metadata associated with the items
- Future work includes releasing human evaluations and datasets for quantitative analysis of design alternatives in RecLLM, as well as expanding capabilities to handle feedback from users through various channels

## Summary

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This paper focuses on building a Conversational Recommender System (CRS) that utilizes Large Language Models (LLMs) to enhance user experience through real-time multi-turn dialogues. By incorporating world knowledge and common-sense reasoning, LLMs enable natural conversations and unlock new possibilities in recommendation systems. 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 roadmap outlines the development of an end-to-end large-scale CRS powered by LLMs with key components including user preference understanding, flexible dialogue management, and explainable recommendations within an integrated architecture. The use of interpretable natural language user profiles allows for improved personalization at the session level. Techniques are also proposed for creating a controllable LLM-based user simulator to generate synthetic conversations due to the lack of conversational data for training. As a proof of concept, RecLLM is introduced as a large-scale CRS for YouTube videos built on LaMDA, showcasing fluency and diverse functionality through example conversations. The paper also delves into the implementation of an LLM ranker module that generates scores and explanations for candidate items based on metadata associated with the items. This involves text summarization using various sources like video transcripts and user comments to provide context for item recommendations. Chain-of-thought reasoning is employed by the LLM ranker to improve classification/regression tasks and generate explanations for recommendation decisions. Future work includes releasing human evaluations and datasets for quantitative analysis of design alternatives in RecLLM, as well as expanding the system's capabilities to handle feedback from users through various channels beyond conversation interactions. Overall, this paper provides insights into harnessing LLMs in building advanced conversational recommender systems with enhanced personalization and transparency features.