Time-Slice Density Estimation for Semantic-Based Tourist Destination Suggestion
Michelangelo Ceci, Annalisa Appice, Donato Malerba · Frontiers in artificial intelligence and applications · 2010
In recent years, a growing interest has been given to trajectory data mining applications that permit to support mobility prediction with the aim of anticipating or pre-fetching possible services [3]. Proposed approaches typically consider only spatio-temporal information provided by collected trajectories. However, in some scenarios, such as that of tourist supporting, semantic information which express needs and interest of the user (tourist) should be taken into account. This semantic information can be extracted from textual documents already consulted by the tourists. In this paper, we present the application of the time-slice density estimation [1] that permits to suggest/predict the next destination of the tourist. In particular, time-slice density estimation permits to measure the rate of change of tourist’s interests at a given geographical position over a user-defined time horizon. Tourist interests depend both on the geographical position of the tourist with respect to a reference system and on semantic information provided by geo-referenced documents associated to the visited sites. Our basic assumption is that a tourist moves towards a close destination which is semantically consistent with her/his current profile as much as possible. Destinations that minimize the profile drift are suggested/predicted as next destinations.