Implicit User Profiling in News Recommender Systems

User profiling is an important part of content-based and hybrid recommender systems. These profiles model users’ interests and preferences and are used to assess an item’s relevance to a particular user. In the news domain it is difficult to extract explicit signals from the users about their interests, and user profiling depends on in-depth analyses of users’ reading habits. This is a challenging task, as news articles have short life spans, are unstructured, and make use of unclear and rapidly changing terminologies. This paper discusses an approach for constructing detailed user profiles on the basis of detailed observations of users’ interaction with a mobile news app. The profiles address both news categories and news entities, distinguish between long-term interests and running context, and are currently used in a real iOS mobile news recommender system that recommends news from 89 Norwegian newspapers.