The Generalized Maximum Coverage Problem
Information Processing Letters
A Personalized Tourist Trip Design Algorithm For Mobile Tourist Guides
Applied Artificial Intelligence
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic construction of travel itineraries using social breadcrumbs
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Travel route recommendation using geotags in photo sharing sites
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Smart itinerary recommendation based on user-generated GPS trajectories
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
The City Trip Planner: An expert system for tourists
Expert Systems with Applications: An International Journal
Trip-Mine: An Efficient Trip Planning Approach with Travel Time Constraints
MDM '11 Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 01
User oriented trajectory search for trip recommendation
Proceedings of the 15th International Conference on Extending Database Technology
How random walks can help tourism
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Social itinerary recommendation from user-generated digital trails
Personal and Ubiquitous Computing
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In this paper we propose TripBuilder, a new framework for personalized touristic tour planning. We mine from Flickr the information about the actual itineraries followed by a multitude of different tourists, and we match these itineraries on the touristic Point of Interests available from Wikipedia. The task of planning personalized touristic tours is then modeled as an instance of the Generalized Maximum Coverage problem. Wisdom-of-the-crowds information allows us to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.