Incorporating context and trends in news recommender systems

Abstract

In our fast changing world, data streams move into the focus. In this paper, we study recommender systems for news portals. Compared with traditional recommender scenarios based on static data sets, the short life cycle of news items and the dynamics in users' preferences are major challenges when developing news recommender systems. This motivates us to research methods facilitating the inclusion of context and trends into news recommender systems. We explain specific requirements for news recommender system and discuss approaches incorporating trends and temporal user habits in order to improve news recommender system. A detailed data analysis motivates our approach. In addition, we discuss experiences of applying news recommendation algorithms online. The evaluation shows that approaches come with specific strengths and weaknesses. Consequently, publishers should select the recommendation strategy with the specific requirements in mind.