Bing-SF-IDF+: Semantics-Driven News Recommendation

Frederik Hogenboom, M Capelle, M (Matthijs) Moerland, Flavius Frasincar

Research output: Contribution to conferencePosterAcademic

1 Citation (Scopus)
4 Downloads (Pure)


Content-based news recommendation is traditionally performed using the cosine similarity and TF-IDF weighting scheme for terms occurring in news messages and user profiles. Semantics-driven variants such as SF-IDF additionally take into account term meaning by exploiting synsets from semantic lexicons. However, they ignore the various semantic relationships between synsets, providing only for a limited understanding of news semantics. Moreover, semantics-based weighting techniques are not able to handle -- often crucial -- named entities, which are usually not present in semantic lexicons. Hence, we extend SF-IDF by also considering the synset semantic relationships, and by employing named entity similarities using Bing page counts. Our proposed method, Bing-SF-IDF+, outperforms TF-IDF and SF-IDF in terms of F1 scores and kappa statistics.
Original languageEnglish
Number of pages2
Publication statusPublished - 7 Apr 2014
EventTwenty-Third International World Wide Web Conference (WWW 2014) -
Duration: 7 Apr 201411 Apr 2014


ConferenceTwenty-Third International World Wide Web Conference (WWW 2014)


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