Recommendation algorithms for online news
Online newspapers are blooming because the online medium offers
advantages that are simply not available in the printed medium.
In particular, online news can be personalized.
With personalization, a user visiting an online newspaper website can
go to a personalized webpage. Different users will see different
versions of the personalized webpage. The content of the personalized
webpage is generated by using a recommender system, which uses a
recommendation algorithm (or a combination thereof) in order to select
articles specifically targeted to the interests of the user.
In this tutorial we review the most commonly used recommendation
algorithms for online news. In particular, we review algorithms based
on collaborative filtering, algorithms based on content-based
filtering, as well as some hybrid approaches.