Travel recommender systems should become flexible advisors with generative ai
Behavior-Mining, Generative Conversations, and Collaborative Advisory: the Future of Travel and Tourism Recommender Systems
Information Retrieval
Summary
Travel recommendation systems help people pick flights, hotels, and destinations, but current ones often use outdated data and focus too much on accuracy, missing things travelers really want. The authors say new systems need to use better data and include new AI methods like conversation generation to act more like helpful advisors. These future tools should also balance the different goals of travelers, service providers, and communities. This approach could improve how travelers plan trips and how travel platforms work.
What this means in practice
- •For travel platform developers: Build travel recommendation tools that act as flexible advisors integrating varied data and generative AI to better support traveler decision making.$Commercial implications: Enables creation of advanced travel planning products that attract travelers by offering personalized, conversational support combining diverse data sources.
- •For local tourism managers: Use improved recommender systems to balance visitor goals with community and sustainability concerns in managing travel demand.
A position paper. It proposes an approach and reports no results.
Authors
Alejandro Bellogín, Linus W. Dietz, Francesco Ricci, Pablo Sánchez
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 We argue that three limitations can explain this: outdated and sparse data sets used to train and validate TTRSs, algorithms that prioritize prediction accuracy over domain-specific dimensions such as novelty and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.