Towards Recommending Interesting Content in News Archives

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11279)


Recently, many archival news article collections have been made available to wide public. However, such collections are typically large, making it difficult for users to find content they would be interested in. Furthermore, archived news articles tend to be perceived by ordinary users as having rather weak attractiveness and being obsolete or uninteresting. In this paper, we propose the task of finding interesting content from news archives and introduce two simple methods for it. Our approach recommends interesting content by comparing the information written in the past with the one from the present.


News archive Interestingness Recommender systems 



This research was supported by MEXT grants (#17H01828; #18K19841; #18H03243).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Social InformaticsKyoto UniversityKyotoJapan
  2. 2.University of FreiburgFreiburg im BreisgauGermany

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