通过整合语言模型和知识图谱技术优化旅游住宿服务

Information Pub Date : 2024-07-10 DOI:10.3390/info15070398
Andrea Cadeddu, Alessandro Chessa, Vincenzo De Leo, Gianni Fenu, Enrico Motta, Francesco Osborne, Diego Reforgiato Recupero, Angelo Salatino, Luca Secchi
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引用次数: 0

摘要

在线平台已成为旅客搜索、比较和预订旅行住宿的主要途径。因此,在线平台和收益管理者必须全面了解这些动态,以提供具有竞争力和吸引力的产品。最近在自然语言处理方面取得的进步,特别是通过开发大型语言模型,在捕捉人类语言错综复杂的细微差别方面取得了重大进展。另一方面,知识图谱已成为表示和组织结构化信息的有力工具。然而,如何有效整合这两种强大的技术仍然是一个持续的挑战。本文介绍了一种创新的深度学习方法,该方法将大型语言模型与特定领域的知识图谱相结合,对旅游报价进行分类。我们系统的主要目标是在以下两个基本方面为收益管理者提供帮助:(i) 理解其住宿产品的市场定位,同时考虑住宿价格、可用性、用户评论和需求等因素;(ii) 优化产品本身的展示和特点,以提高其整体吸引力。为此,我们开发了一个涵盖各种住宿信息的领域知识图谱,并实施了有针对性的特征工程技术,以增强大型语言模型中的信息表示。为了评估我们的方法的有效性,我们在有关伦敦住宿信息的四个数据集上与其他方法进行了比较分析。所提出的解决方案取得了出色的结果,明显优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Tourism Accommodation Offers by Integrating Language Models and Knowledge Graph Technologies
Online platforms have become the primary means for travellers to search, compare, and book accommodations for their trips. Consequently, online platforms and revenue managers must acquire a comprehensive comprehension of these dynamics to formulate a competitive and appealing offerings. Recent advancements in natural language processing, specifically through the development of large language models, have demonstrated significant progress in capturing the intricate nuances of human language. On the other hand, knowledge graphs have emerged as potent instruments for representing and organizing structured information. Nevertheless, effectively integrating these two powerful technologies remains an ongoing challenge. This paper presents an innovative deep learning methodology that combines large language models with domain-specific knowledge graphs for classification of tourism offers. The main objective of our system is to assist revenue managers in the following two fundamental dimensions: (i) comprehending the market positioning of their accommodation offerings, taking into consideration factors such as accommodation price and availability, together with user reviews and demand, and (ii) optimizing presentations and characteristics of the offerings themselves, with the intention of improving their overall appeal. For this purpose, we developed a domain knowledge graph covering a variety of information about accommodations and implemented targeted feature engineering techniques to enhance the information representation within a large language model. To evaluate the effectiveness of our approach, we conducted a comparative analysis against alternative methods on four datasets about accommodation offers in London. The proposed solution obtained excellent results, significantly outperforming alternative methods.
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