Learning User Profiles In Mobile News Recommendation

Mobile news recommender systems help users retrieve relevant news stories from numerous news sources with minimal user interaction. The overall objective is to find ways of representing news stories, users and their relationships that allow the system to predict which news would be interesting to read for which users. Even though research shows that the quality of these recommendations depends on good user profiles, most systems have no or very simple profiles, because users are reluctant to giving explicit feedback on articles' desirability. In this paper we present a user profiling approach adopted in the SmartMedia news recommendation project. We are building a mobile news recommender app that sources news from all major Norwegian newspapers and uses a hybrid recommendation strategy to rank the news according to the users' context and interests. The user profiles in SmartMedia are built in real-time on the basis of implicit feedback from the users and contain information about the users' general interests in news categories and particular interests in events or entities. Experiments with content-based filtering show that the profiles lead to more targeted recommendations and provide an efficient way of monitoring and representing users' interests over time.