Semantic-Based Destination Suggestion in Intelligent Tourism Information Systems

Abstract

Abstract. In recent years, there has been a growing interest in mining trajectories of moving objects. Advances in this data mining task are likely to support the development of new applications such as mobility prediction and service pre-fetching. Approaches reported in the literature consider only spatio-temporal information provided by collected trajectories. However, some applications demand additional sources of information to make correct predictions. In this work, we consider the case of an on-line tourist support service which aims at suggesting places to visit in the nearby. We assume tourist interests depend both on her/his geographical position and on the “semantic ” information extracted from geo-referenced documents associated to the visited sites. Therefore, the suggestion is based on both spatio-temporal data as well as on textual data. To deal with tourist’s interest drift we apply a time-slice density estimation method. Experimental results are reported for two scenarios.