News Recommendations using CF-IDF

Frederik Hogenboom, Flavius Frasincar, U Kaymak, Franciska de Jong

Research output: Contribution to conferenceAbstractAcademic


Most of the traditional recommendation algorithms are based on TF-IDF, a term-based weighting method. This paper proposes a new method for recommending news items based on the weighting of the occurrences of references to concepts, which we call Concept Frequency-Inverse Document Frequency (CF-IDF). In an experimental setup we apply CF-IDF to a set of newswires in which we detect 1,167 instances of a set of 65 concepts from a domain ontology. The proposed method yields significantly better results with respect to accuracy, recall, and F1 than the TF-IDF method we use as a basis for comparison.
Original languageEnglish
Number of pages2
Publication statusPublished - 2 Nov 2011
EventTwenty-Third Benelux Conference on Artificial Intelligence (BNAIC 2011) - Gent, Belgium
Duration: 2 Nov 20113 Nov 2011


ConferenceTwenty-Third Benelux Conference on Artificial Intelligence (BNAIC 2011)
CityGent, Belgium


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