VideolandGPT: A User Study on a Conversational Recommender System
- URL: http://arxiv.org/abs/2309.03645v1
- Date: Thu, 7 Sep 2023 11:24:47 GMT
- Title: VideolandGPT: A User Study on a Conversational Recommender System
- Authors: Mateo Gutierrez Granada, Dina Zilbershtein, Daan Odijk, Francesco
Barile
- Abstract summary: 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.
We evaluate ranking metrics, user experience, and fairness of recommendations, comparing a personalised and a non-personalised version of the system.
- Score: 0.14495144578817493
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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