Knowledge modeling for personalized travel recommender

Abstract

Looking for information for travel or holiday destinations can be troublesome when the information gathered is often too much and when this happens, a traveler has to spend a lot of time to filter or digest the information before a sensible travel plan can be prepared. A traveler normally needs more than one piece of travel information to decide on destinations to visit and a collection of such information is normally extracted from several information sources such as travel guides, forums, blogs, social applications and websites of accommodations, transportations and eating places. In this paper, we present a mobile personalized travel recommender that is known as Travel Advisor. It is designed to provide a context-aware support for itinerary planning for both individual and group travelers according to the context, static profile, and dynamic profile of the travelers. It leverages on the use of semantic knowledge representations to make semantically meaningful recommendations to meet the need of travelers. This paper also describes the challenges associated with personalized travel recommendations. © 2012 Springer-Verlag.