Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.

翻译：推荐系统已成为酒店业不可或缺的工具，能够为客人提供个性化且量身定制的体验。大型语言模型（如ChatGPT）和说服性技术的最新进展，为提升这些系统的有效性开辟了新途径。本文探讨了整合ChatGPT与说服性技术以自动化和改进酒店推荐系统的潜力。首先，我们深入分析了ChatGPT的能力，该模型能够理解并生成类人文本，从而实现更准确且更具情境感知的推荐。我们讨论了将ChatGPT集成到推荐系统的方案，强调其分析用户偏好、从在线评论中提取有价值信息、并基于客人档案生成个性化推荐的能力。其次，我们研究了说服性技术在影响用户行为及增强酒店推荐说服效果中的作用。通过融入社会认同、稀缺性和个性化等说服技巧，推荐系统可有效影响用户决策，并鼓励采取预订特定酒店或升级房间等期望行为。为探究ChatGPT与说服性技术的有效性，我们开展了一项包含酒店推荐系统案例研究的初步实验，旨在分析整合ChatGPT与说服技巧对用户参与度、满意度和转化率的影响。初步结果显示了这些技术在提升整体宾客体验与商业绩效方面的潜力。总体而言，本文通过探索推荐系统中大型语言模型与说服性技术的协同关系，为酒店业领域做出贡献，最终影响宾客满意度与酒店收入。