[论文解读] ChatGPT and Persuasive Technologies for the Management and Delivery of Personalized Recommendations in Hotel Hospitality

Manolis Remountakis, Konstantinos Kotis+2 more|arXiv (Cornell University)|Jul 26, 2023

Recommender Systems and Techniques被引用 6

一句话总结

本文提出将ChatGPT与说服性技术整合至酒店推荐系统，以增强个性化和用户参与度。通过利用大语言模型生成上下文感知的推荐内容，并结合社会认同与稀缺性等说服性技术，系统在试点案例研究中显著提升了宾客满意度与转化率，用户参与度与预订意愿均有可衡量的提升。

ABSTRACT

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.

研究动机与目标

• 探究将类似ChatGPT的大语言模型整合至酒店推荐系统，以提升个性化水平。
• 研究说服性技术（如社会认同、稀缺性与个性化）如何增强推荐对宾客决策的影响。
• 评估大语言模型生成的推荐与说服性设计相结合对用户参与度、满意度及转化率的综合影响，场景设定为真实酒店环境。
• 展示一种基于人工智能、行为驱动的推荐系统在酒店业中的可行性与有效性。

提出的方法

• 利用ChatGPT的自然语言理解与生成能力，解析用户档案，并从在线评论中提取偏好。
• 设计一种混合推荐系统，结合基于检索与生成的AI组件，实现上下文感知的推荐推送。
• 在推荐输出中融入说服性设计元素，如稀缺性提示、社会认同（例如：'本周已有500位宾客预订此房间'）及个性化信息。
• 在真实酒店系统中实施试点案例研究，评估用户互动、满意度与转化率指标。
• 收集并分析用户参与数据，包括任务耗时、选择率与预订行为，以评估系统性能。

实验结果

研究问题

• RQ1 与传统系统相比，集成ChatGPT在多大程度上提升了酒店推荐的相关性与个性化水平？
• RQ2 在酒店推荐场景中，说服性设计技术在多大程度上提升了用户参与度与转化率？
• RQ3 大语言模型生成内容与说服性元素相结合，对宾客满意度与预订意愿的综合影响如何？
• RQ4 用户对包含社会认同与稀缺性提示的AI生成推荐有何反应？

主要发现

• ChatGPT的集成显著提升了推荐的上下文相关性与自然语言质量，使其更符合用户偏好。
• 引入说服性技术后，用户参与度明显提升，参与者花在浏览推荐选项上的时间更长。
• 试点研究中转化率有所提高，尤其在中高端房型升级方面表现突出，表明对预订决策具有更强影响力。
• 用户对结合个性化语言与说服性提示的推荐表现出更高的满意度，表明其感知价值得到增强。