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A tourism dataset from historical transaction for recommender systems
Choirul Huda1, Yaya Heryadi1, Lukas2
1Computer Science Department, Bina Nusantara University, Jakarta 11480, Indonesia.
Data in Brief
|January 16, 2024
Summary
This study processed raw TripAdvisor data to create a valuable tourism dataset. This dataset supports the development of effective recommender systems for travel recommendations.
Area of Science:
- Data Science
- Tourism Informatics
Background:
- The tourism industry's growth necessitates advanced data utilization.
- Recommender systems rely on historical transaction data for personalized suggestions.
Purpose of the Study:
- To process unstructured tourism data from TripAdvisor into a structured dataset.
- To facilitate the development of tourism recommender systems.
Main Methods:
- Data restructuring, validation, and content enrichment.
- Integration with Google Maps and data normalization.
- Development of an entity relational model for Indonesian tourism.
Main Results:
- An original dataset containing user transactions, attraction details, and geographical information was created.
- The dataset includes specifics on attraction types, continents, regions, countries, cities, and visiting modes.
- An entity relational model was established for key Indonesian tourist destinations.
Conclusions:
- The processed dataset is suitable for building tourism recommender systems.
- This work provides a foundation for data-driven tourism recommendations, particularly for Indonesia.
Keywords:
Collaborative filteringHistorical transactionItemRatingRecommender systemTourism datasetUserMore Related Videos
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