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
the challenges of news recommendation.