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2015
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Bing-SF-IDF+: a hybrid semantics-driven news recommender

3 years 6 months ago
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.
Michel Capelle, Marnix Moerland, Frederik Hogenboo
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where SAC
Authors Michel Capelle, Marnix Moerland, Frederik Hogenboom, Flavius Frasincar, Damir Vandic
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