AI 中文总结
本文指出旅游推荐系统因数据过时、算法忽视领域维度及未满足旅行者需求而未能普及，受生成式AI冲击，主张未来系统应成为整合多数据类型、平衡多方利益的灵活顾问。
AI 中文摘要
自电子商务早期采用以来，旅游与旅行一直是推荐系统设计的试验场：这些工具帮助旅行者选择目的地、航班、住宿，并将其组合成行程。从基于案例的推理到强化学习，数据驱动的推荐技术已被调整以适应旅行者的需求。研究社区已经产生了多方面的旅游与旅行推荐系统（TTRSs）原型，这些系统具有上下文依赖性、面向多利益相关者，并且最近开始解决诸如过度旅游等可持续性问题。尽管有这些持续的工作，TTRSs尚未广泛普及。我们认为三个局限性可以解释这一点：用于训练和验证TTRSs的数据集过时且稀疏，算法优先考虑预测准确性而非领域特定维度（如新颖性和上下文相关性），以及未能解决旅行者的具体需求。有针对性的增量研究可以解决这些局限性，但与此同时，一个颠覆性因素已进入旅游信息和商业化平台的生态系统：生成式人工智能。根据市场研究，生成式AI应用正成为旅行者规划行程的主要入口。这迫使研究重新思考TTRSs应如何设计以及应整合哪些核心技术。我们声称，未来的TTRSs除了提供个性化信息过滤外，还应成为更灵活的顾问，支持决策制定，整合多种数据类型和AI技术，从数据挖掘到自然语言处理。此外，它们必须透明地平衡旅行者、服务供应商、平台所有者和当地社区的冲突目标。然后，我们概述了构建更有效的TTRSs的研究目标，有效结合新旧推荐技术。
英文摘要
Since the early adoption of e-commerce, travel and tourism has been a lab for the design of recommender systems: tools that help travelers choose destinations, flights, accommodations, and combine them into itineraries. Data-driven recommendation techniques, ranging from case-based reasoning to reinforcement learning, have been adapted to travelers' needs. The research community has produced multifaceted prototypes of travel and tourism recommender systems (TTRSs), which are context-dependent, multistakeholder-oriented, and more recently, addressing sustainability issues, such as overtourism. Despite this enduring work, TTRSs are not widespread yet. We argue that three limitations can explain this: outdated and sparse data sets used to train and validate TTRSs, algorithms that prioritize prediction accuracy over domain-specific dimensions such as novelty and contextual relevance, and a failure to address the specific needs of travelers. Targeted incremental research could address these limitations, but a disruptive factor has meanwhile entered the ecosystem of tourism information and commercialization platforms: generative artificial intelligence. According to market research, GenAI applications are becoming the primary entry point for travelers planning their trips. This forces research to rethink how TTRSs should be designed and which core techniques should be integrated. We claim that future TTRSs, in addition to offering personalized information filtering, should become more flexible advisors that support decision making, integrating multiple data types and AI techniques, from data mining to natural language processing. Moreover, they must transparently balance the conflicting goals of travelers, service suppliers, platform owners, and local communities. We then outline research targets for building more effective TTRSs, fruitfully combining old and new recommendation techniques.
