The increasing number of data a booking platform such as Booking.com and AirBnB offers make it challenging for interested parties to browse through the available accommodations and analyze reviews in an efficient way. Efforts have been made from the booking platform providers to utilize recommender systems in an effort to enable the user to filter the results by factors such as stars, amenities, cost but most valuable insights can be provided by the unstructured text-based reviews. Going through these reviews one-by-one requires a substantial amount of time to be devoted while a respectable percentage of the reviews won't provide to the user what they are actually looking for. This research publication explores how Large Language Models (LLMs) can enhance short rental apartments recommendations by summarizing and mining key insights from user reviews. The web application presented in this paper, named "instaGuide", automates the procedure of isolating the text-based user reviews from a property on the Booking.com platform, synthesizing the summary of the reviews, and enabling the user to query specific aspects of the property in an effort to gain feedback on their personal questions/criteria. During the development of the instaGuide tool, numerous LLM models were evaluated based on accuracy, cost, and response quality. The results suggest that the LLM-powered summarization reduces significantly the amount of time the users need to devote on their search for the right short rental apartment, improving the overall decision-making procedure.

翻译：随着Booking.com和AirBnB等预订平台数据量的日益增长，用户难以高效浏览可用住宿选项并分析海量评论。虽然平台提供商已通过推荐系统使用户能按星级、设施、成本等因素筛选结果，但最具价值的洞察往往蕴含在非结构化的文本评论中。逐条阅读这些评论需耗费大量时间，且相当比例的评论无法提供用户真正需要的信息。本研究探讨如何利用大语言模型（LLMs）通过总结和挖掘用户评论中的关键信息来增强短租公寓推荐。本文提出的名为"instaGuide"的Web应用程序，实现了从Booking.com平台自动提取房源文本评论、生成评论摘要，并允许用户针对房源特定方面进行查询以获取个性化问题/标准的反馈。在instaGuide工具开发过程中，我们基于准确性、成本和响应质量评估了多种LLM模型。结果表明，采用LLM驱动的摘要技术能显著减少用户寻找合适短租公寓所需的时间，从而优化整体决策流程。