This paper investigates how large language models (LLMs) can enhance recommender systems, with a specific focus on Conversational Recommender Systems that leverage user preferences and personalised candidate selections from existing ranking models. We introduce VideolandGPT, a recommender system for a Video-on-Demand (VOD) platform, Videoland, which uses ChatGPT to select from a predetermined set of contents, considering the additional context indicated by users' interactions with a chat interface. We evaluate ranking metrics, user experience, and fairness of recommendations, comparing a personalised and a non-personalised version of the system, in a between-subject user study. Our results indicate that the personalised version outperforms the non-personalised in terms of accuracy and general user satisfaction, while both versions increase the visibility of items which are not in the top of the recommendation lists. However, both versions present inconsistent behavior in terms of fairness, as the system may generate recommendations which are not available on Videoland.
@article{arxiv.2309.03645,
title = {VideolandGPT: A User Study on a Conversational Recommender System},
author = {Mateo Gutierrez Granada and Dina Zilbershtein and Daan Odijk and Francesco Barile},
journal= {arXiv preprint arXiv:2309.03645},
year = {2023}
}
Comments
Preprint for KARS2023 (5th Knowledge-aware and Conversational Recommender Systems Workshop at RecSys2023)