Guiding the Journey: Innovative Recommender Systems for Personalized Tourism, Travel, and Hospitality Experiences

Tourism, travel, and hospitality services have become integral parts of our globalized world. With the advent of technology and the rise of the internet, users now have access to an overwhelming amount of information when it comes to travel planning. The challenge lies in helping users navigate through this vast sea of options and making personalized recommendations based on their preferences. This is where recommender systems come into play. They offer users tailor-made suggestions for travel destinations, activities, and accommodations based on their interests, past experiences, and available budget. By leveraging machine learning algorithms and user profiling techniques, these systems can significantly enhance the travel planning process, resulting in more enjoyable and fulfilling experiences for users.

Considerable progress has already been made in this field. Existing research has focused on various aspects, including collaborative filtering methods, content-based filtering approaches, and hybrid models that combine both techniques. However, much more exploration is needed to overcome the challenges that persist. These challenges include improving the accuracy of recommendations, addressing the cold-start problem for new users or destinations, and enhancing the scalability and adaptability of recommender systems to handle the vast amount of travel-related data.

Currently, the research in this field is focused on advancing recommender systems in the tourism, travel, and hospitality domains. The goal is to develop more sophisticated algorithms and methodologies that can provide users with increasingly accurate and personalized recommendations. Researchers are working towards overcoming existing limitations and tackling emerging challenges to create robust systems capable of handling diverse user preferences, dynamic user behaviour and various contextual factors.

The areas of research that need further exploration include but are not limited to:

• Novel recommendation algorithms for tourism, travel, and hospitality services
• Context-aware recommender systems for personalized activity suggestions considering factors such as location, time, and social context
• User modelling and profiling techniques to capture complex user preferences and behaviours
• Handling the cold-start problem for new users and destinations
• Incorporating sentiment analysis and user feedback into recommender systems
• Evaluating the impact of recommender systems on user satisfaction and the overall tourism industry
• Evaluation metrics for measuring the effectiveness of tourism recommender systems
• Collaborative filtering techniques for travel recommendation
• Content-based filtering approaches for personalized accommodation recommendations
• Hybrid recommendation models for travel planning

We aim for this Research Topic to foster greater collaboration and knowledge sharing among experts in this field, ultimately pushing the boundaries of recommender systems for the tourism, travel, and hospitality industry.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Recommender Systems, Personalized Tourism, User Modeling, Travel Recommendations, RecSys

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.