Conversational Recommender Systems based on Extracting Implicit Preferences with Large Language Models

Abstract

Conversational recommender systems (CRS) have gained significant attention for their ability to provide personalized recommendations through conversational interfaces. CRS are increasingly being used in various fields such as e-commerce, entertainment, and customer services by understanding user preferences and providing personalized recommendations. Large Language Models (LLMs) have potential in recommendation systems due to their ability to understand and generate text, as well as their generalization and reasoning capabilities. In this paper, we propose a novel method that leverages LLMs to extract implicit information from conversations and explicitly incorporate it into recommendations. Our approach focuses on extracting implicit information such as user-preferred categories from conversations and explicitly adding it to the recommendation processes to enhance performance. We utilized Reddit-movie dataset, which provides rich conversational data, to extract users’ implicit preferred movie genres from conversations and explicitly incorporate this information into the conversation to recommend movies. Experimental results show that both GPT-3.5-turbo and GPT-4 models perform exceptionally well at identifying user preferences and providing accurate recommendations. These findings demonstrate that utilizing implicit information extracted from conversations can effectively enhance recommendation quality, highlighting the potential of LLMs in conversational recommender systems. © 2024 Copyright for this paper by its authors.