ChatGPT as a Conversational Recommender System: A User-Centric Analysis

Abstract

With the rapid advances in deep learning, we have witnessed a strongly increased interest in conversational recommender systems (CRS). Until recently, however, even the latest generative models exhibited major limitations and they frequently return non-meaningful responses according to previous studies. However, with the latest Generative AI-based dialog systems implemented with Generative Pre-Trained Transformer (GPT) models, a new era has arrived for CRS research. In this work, we study the use of ChatGPT as a movie recommender system. To this purpose, we conducted an online user study involving N=190 participants, who were tasked to evaluate ChatGPT’s responses in a multitude of dialog situations. As a reference point for the analysis, we included a retrieval-based conversational method in the experiment, which was found to be a robust approach in previous research.

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