Bing-CF-IDF+: A Semantics-Driven News Recommender System

  • Emma Brocken
  • Aron Hartveld
  • Emma de Koning
  • Thomas van Noort
  • Frederik Hogenboom
  • Flavius FrasincarEmail author
  • Tarmo Robal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)


With the ever growing amount of news on the Web, the need for automatically finding the relevant content increases. Semantics-driven news recommender systems suggest unread items to users by matching user profiles, which are based on information found in previously read articles, with emerging news. This paper proposes an extension to the state-of-the-art semantics-driven CF-IDF+ news recommender system, which uses identified news item concepts and their related concepts for constructing user profiles and processing unread news messages. Due to its domain specificity and reliance on knowledge bases, such a concept-based recommender neglects many highly frequent named entities found in news items, which contain relevant information about a news item’s content. Therefore, we extend the CF-IDF+ recommender by adding information found in named entities, through the employment of a Bing-based distance measure. Our Bing-CF-IDF+ recommender outperforms the classic TF-IDF and the concept-based CF-IDF and CF-IDF+ recommenders in terms of the \(F_1\)-score and the Kappa statistic.


News recommendation system Content-based recommender Semantic Web Named entities Bing-CF-IDF+ 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Erasmus University RotterdamRotterdamThe Netherlands
  2. 2.Tallinn University of TechnologyTallinnEstonia

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