Behavior-Mining, Generative Conversations, and Collaborative Advisory: the Future of Travel and Tourism Recommender Systems

2026-10-06

AI Summary

This study addresses the adoption bottlenecks in tourism recommender systems caused by data sparsity, insufficient context awareness, and limited personalization. To overcome these challenges, this work proposes a next-generation recommendation framework that integrates generative artificial intelligence with data mining techniques. Methodologically, it leverages generative AI as the core interactive interface while incorporating behavior mining, natural language processing, multi-source data fusion, and collaborative filtering to reconstruct conventional recommendation paradigms. The primary contribution lies in establishing a transparent, collaborative advisory mechanism that reconciles conflicting interests among tourists, service providers, and local communities. This mechanism enables more precise personalized information filtering and enhanced decision support, ultimately providing a systematic research framework for the intelligent evolution of tourism recommender systems.

Abstract

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 We argue that three limitations can explain this: outdated and sparse data sets used to train and validate TTRSs, algorithms that prioritize prediction accuracy over domain-specific dimensions such as novelty and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.

Problem

• Travel and Tourism Recommender Systems
• Generative AI
• Multi-stakeholder Optimization
• Data Sparsity

Innovation

• Generative AI
• Travel Recommender Systems
• Multi-stakeholder Optimization
• Natural Language Processing
• Behavior Mining