Recommending news articles using rule-based classifier

In this paper we summarize our experiments with a rule-based classi-fier as a recommender within CLEF NewsREEL 2017 challenge. Systems that recommend news articles are suitable to solve information overflow in digital editions of newspapers, when users have problems choosing what they want to read. They face challenges unknown to the systems recommending books or movies such as a frequency of producing the new content. This paper deals with an approach based on association rules acting as a classifier. In our approach we experimented with settings that allow reducing the amount of rules used for the classification and increasing the performance that is crucial for real recommen-dations