Personalized news recommendation with context trees

Paper i proceeding, 2013

The proliferation of online news creates a need for altering interesting articles. Compared to other products, however, recommending news has specific challenges: news preferences are subject to trends, users do not want to see multiple articles with similar content, and frequently we have insufficient information to prolfie the reader. In this paper, we introduce a class of news recommendation systems based on context trees. They can provide highquality news recommendations to anonymous visitors based on present browsing behaviour. Using an unbiased testing methodology, we show that they make accurate and novel recommendations, and that they are sufficiently exible for the challenges of news recommendation.