The tourism industry has been drastically transformed by the rapid development of information and communication technologies (ICT) and the Web, leading to the emergence of new online intermediaries that compete with and replace traditional travel agents and agencies. Today’s travelers actively seek out information and customize their vacation packages according to their individual preferences. Even while on-site, they rely heavily on digital companions and tools to plan, organize, and enjoy their stay.
Recommender systems have become essential tools for helping users overcome information overload and match them with a diverse range of tourism services, including transportation, accommodation, activities, and events. However, designing recommender systems for the tourism domain is much more complex than designing them for online commerce, as it involves searching for interconnected products with limited availability and contextual factors that greatly influence decision-making, such as time, location, social and environmental contexts. Additionally, tourism products are experience goods and emotionally charged, meaning that decision-making is not solely based on rational and objective criteria. Consequently, providing the right information to visitors at the right time about the site and various nearby services is challenging.
RecTour 2023 aims to address these specific challenges for recommender systems in tourism by bringing together researchers and practitioners from various fields, including tourism, recommender systems, user modeling, user interaction, mobile, ubiquitous and ambient technologies, artificial intelligence, and web information systems. The conference will focus on discussing and illustrating challenges and applications of these technologies in tourism recommender systems of the future. Important aspects and topics to be discussed revolve around (but are not limited to):
- Specific applications and case studies (evaluation);
- Specific methods and techniques for tourism recommenders;
- Novel ICT and their impact on travel and tourism;
- Data integration from various sources (e.g., catalogues, Linked Open Data, usage logs);
- Context and mobility in tourism;
- Tourist trip recommendation and route planning;
- Cold-start problem in the context of tourism recommenders;
- Preference elicitation in tourism;
- Emotions and tourism recommenders;
- Personalized interaction and conversation strategies;
- Repercussions of COVID-19.