Bing-SF-IDF+: A hybrid semantics-driven news recommender

Content-based news recommendation is traditionally performed using the cosine similarity and the 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, semanticsbased weighting techniques are not able to handle-often crucial-named entities, which are often 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 based on a news data set. Copyright is held by the owner/author(s).

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