Avoiding the Overspecialization of Recommender Systems in Tourism with Semantic Trajectories, Initial Thoughts

Abstract

Nowadays, recommender systems are at use in various domains of everyday life such as social media networks, video on demand platforms or tourism. They help users sorting a vast amount of items and then get a more satisfying experience. However, these recommender systems tend to have a bias in the items recommended, a situation known as the overspecialization or diversity problem. In the tourism domain, this means new points of interest are less likely to be recommended than already established and well known places and that tourists tend to have the same trip over the same places, making it less personal. This paper presents and discusses first thoughts on how to overcome the overspecialization problem in the tourism domain by using the notion of ”semantic trajectory” of tourists in a touristic area.