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 timebudget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.
We present an application where semantically enriched trajectories obtained from crowdsensed data are used to build an advanced system for planning personalized sightseeing tours, called TripBuilder. The interesting feature of TripBuilder is that it uses Wikipedia content and trajectories of previous tourists collected by georeferenced Flickr photos in a complex spatio-temporal framework. The objective is to address, in an unsupervised way, the problem of suggesting a budgeted sightseeing tour based on the preferences of the tourist and the time available for the visit. We present few highlights of how TripBuilder works along with a research agenda where we discuss the role of semantically enriched trajectories and crowdsourced location data in planning itineraries.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.