The tourism industry stands at the intersection of rapid technological advancements and evolving consumer behaviors, reshaping the way individuals explore, plan, and experience travel. The proliferation of information and communication technologies (ICT) and the widespread adoption of online platforms have ushered in a new era of travel, empowering travelers to curate bespoke experiences tailored to their unique preferences and interests. From researching destinations to booking accommodations and activities, today’s travelers rely heavily on digital companions and recommendation systems to navigate the vast landscape of tourism services and offerings.
Recommender systems have emerged as indispensable tools in the modern travel ecosystem, offering users personalized recommendations and alleviating the burden of information overload. However, designing effective recommender systems for the tourism domain presents a myriad of challenges distinct from traditional e-commerce contexts. In tourism, recommendations must account for interconnected products with limited availability, contextual factors such as time, location, and social dynamics, and the emotive nature of travel decision-making. Crafting recommendations that resonate with users’ experiential preferences and emotional aspirations requires a nuanced understanding of their needs and desires.
RecTour 2024 seeks to delve deeper into the evolving landscape of tourism recommendation systems by convening researchers and practitioners from diverse disciplines. By fostering interdisciplinary dialogue among experts in tourism, recommender systems, user modeling, artificial intelligence, and beyond, the conference aims to address the unique challenges and opportunities inherent in designing next-generation tourism recommendation systems.
Topics of Interest:
- Personalization in Tourism Recommendations: Delve into advancements in personalized recommendation algorithms tailored to individual preferences, encompassing accommodation, activities, dining, and cultural experiences.
- Contextual Intelligence: Explore the integration of contextual factors such as location, time, social dynamics, and environmental conditions into recommendation systems to enhance relevance and user satisfaction.
- Multi-Modal Recommender Systems: Investigate the fusion of diverse data sources, including catalogues, user-generated content, and sensor data, to create comprehensive and adaptive recommendation frameworks.
- Dynamic Pricing and Economics: Examine the intersection of recommendation systems with economic principles, including dynamic pricing models, personalized promotion optimization etc.
- User Interaction and Experience: Discuss innovative approaches to enhancing user engagement and satisfaction through interactive interfaces, conversational agents, and immersive technologies.
- Ethical and Responsible Recommendations: Address the ethical considerations and implications of recommendation systems in tourism, including privacy concerns, fairness, and transparency.
- Emerging Technologies: Explore the transformative potential of emerging technologies, including Large Language Models (LLMs) on the future of tourism recommendation systems.
- Emotion in Tourism Recommendations: Explore the role of emotion in shaping travel decisions and the integration of affective computing techniques into recommendation systems to enhance user satisfaction and engagement.
- Addressing the Cold Start Problem: Investigate strategies and algorithms to mitigate the cold start challenge in tourism recommendation systems, particularly for new users with limited historical data, ensuring personalized and relevant recommendations from the outset.
- Leveraging Social Media Insights: Investigate the utilization of social media data and insights in tourism recommendation systems. Explore how social media platforms can provide valuable user-generated content, sentiment analysis, and social network information to enhance the personalization and relevance of recommendations.