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.

翻译：本文探讨了大型语言模型如何增强推荐系统，特别聚焦于利用用户偏好及现有排序模型中个性化候选选择的对话式推荐系统。我们介绍VideolandGPT——面向视频点播平台Videoland的推荐系统，该系统借助ChatGPT从预设内容集中进行选择，并综合考虑用户通过聊天界面交互所呈现的额外语境。通过一项组间用户研究，我们评估了系统在排名指标、用户体验和推荐公平性方面的表现，对比了个性化版本与非个性化版本。结果表明，个性化版本在准确性和整体用户满意度上均优于非个性化版本，且两个版本均提升了推荐列表中非头部内容的可见性。然而，两个版本在公平性方面均表现出不一致性，因为系统可能生成Videoland平台上不可用的推荐项。