Abstract

Currently, tourism providers build tourist models by collecting some specific pieces of information and then combining the knowledge they have about the groups to which current tourists belong. This paper presents the BaliaTour user modelling and Recommender System, which combines several techniques and methodologies in order to enhance the modelling process when scarce of data about an individual tourist is available. The core of the model is based on the predictions made over stereotypes as a initial characterization of the user profile. The modelling is further refined and enhanced by the combination of explicit preferences and ratings provided by the user. As a result, the proposed approach takes advantage of every information piece known about tourists in tourism ecosystems. The main advantage of BaliaTour is to minimize the main drawbacks of each of the existing user modelling techniques to obtain a user model.

BIB_text

@Article {
author = {Isabel Torre, María Teresa Linaza, Ander García},
title = {Hybrid User Modelling Algorithms for Tourism Providers},
pages = {483-494},
keywds = {

Hybrid user model; Stereotypes; Explicit preferences; Tourism entity

}
abstract = {

}
date = {2013-01-23},
year = {2013},
}