Enhancing Hotel Recommendations with AI: LLM-Based Review Summarization and Query-Driven Insights

2025-10-21

AI Summary

To address the challenge of inefficiently leveraging massive unstructured user reviews on short-term rental platforms (e.g., Booking.com), this paper proposes an LLM-based review understanding and interactive recommendation framework, implemented as a web application named instaGuide. The system automatically crawls and parses raw reviews, integrating multi-LLM collaborative summarization with query-driven information retrieval to deliver real-time, structured insights aligned with user-specified criteria (e.g., “quiet,” “family-friendly”). Its key contribution lies in transcending conventional recommendation systems’ reliance on predefined structured features—achieving, for the first time, fine-grained, comment-level semantic understanding and dynamic, question-answering–enabled interaction. Experimental evaluation demonstrates that the approach achieves an optimal trade-off among accuracy, response quality, and computational efficiency, significantly reducing users’ decision latency and enhancing accommodation selection effectiveness.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Summarizing hotel reviews using LLMs to save users time
• Extracting key insights from unstructured text-based accommodation reviews
• Enabling query-driven analysis of rental properties for better decisions

Innovation

• LLMs summarize user reviews for rental recommendations
• Automated query system answers specific property questions
• Evaluated multiple LLMs for accuracy and cost efficiency