Enhancing Hotel Recommendations with AI: LLM-Based Review Summarization and Query-Driven Insights
Nikolaos Belibasakis, Anastasios Giannaros, Ioanna Giannoukou, Spyros Sioutas
TL;DR
This paper presents instaGuide, an AI-powered web application that extracts Booking.com reviews, generates concise summaries, and supports user-driven questions to streamline short-rental property selection. It combines web scraping or API-based retrieval with Retrieval-Augmented Generation (RAG) and multiple Large Language Models (LLMs) to deliver fast, topic-focused insights across languages. Through comparative evaluation of several LLMs and a small user study, the approach demonstrates substantial reductions in time spent evaluating properties and highlights practical trade-offs in cost, speed, and reliability. The work underscores the potential of LLM+RAG for transparent, user-centered recommender tools in marketplaces with abundant unstructured text, and it points to broader applicability beyond travel to other domains.
Abstract
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.