News Recommendation in Real-Time


Recommender systems support users facing information overload situations. Typically, such situations arise as users have to choose between an immense number of alternatives. Examples include deciding what songs to listen to, what movies to watch, and what news article to read. In this chapter, we outline the case of suggesting news articles. This task entails a number of challenges. First, news collections do not remain relevant unlike movies or songs. Users continue to request novel contents. Second, users avoid creating consistent profiles thus reject login procedures. Third, requests arrive in enormous streams. Having short consumption times, users quickly request the next article to read. Handling these challenges requires adaptations to existing recommendation strategies as well as developing novel ones.

author = {...},
booktitle = {...},
chapter = {...},
editor = {...},
organization = {...},
publisher = {...}, 
series = {...},
volume = {...},
year = {...},
ISBN = {...},
ISSN = {...},
Book Contribution
Springer Verlag