KGNR: A knowledge-based geographical news recommender

Abstract

Online news reading services, such as Google News and Yahoo! News, have become very popular since the Internet provides fast access to news articles from various sources around the world. A key issue of these services is to help users to find interesting articles that match their preferences as much as possible. This is the problem of personalized news recommendation. Recently, personalized news recommendation has become a promising research direction and a variety of techniques have been proposed to tackle it, including content-based systems, collaborative filtering systems and hybrid versions of these two. In addition, the widespread use of mobile phones today and the different features that these phones offer users allow the possibility to keep users up to date with the latest news that have taken place in their environment, anywhere and at any time. This paper presents KGNR (Knowledge-based Geographical News Recommender), a new approach to develop a personalized news recommendation system as an application for mobile phones that takes into account the geolocation of the user and uses learned user profiles to generate personalized news recommendations. For this purpose, a content-based recommendation mechanism have been combined with topic-maps and geolocation for modeling the recommendation system.