2014
News recommendation using semantics with the bing-sf-idf approach
Publication
Publication
Traditionally, content-based news recommendation is performed by means of the cosine similarity and the TF-IDF weighting scheme for terms occurring in news messages and user profiles. Semanticsdriven variants like SF-IDF additionally take into account term meaning by exploiting synsets from semantic lexicons. However, semantics-based 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 employing named entity similarities using Bing page counts. Our proposed method, Bing-SF-IDF, outperforms TF-IDF and its semantics-driven variants in terms of F<inf>1</inf>-scores and kappa statistics.
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hdl.handle.net/1765/85379 | |
Organisation | Erasmus University Rotterdam |
Hogenboom, F., Capelle, M., & Moerland, M. (2014). News recommendation using semantics with the bing-sf-idf approach. Retrieved from http://hdl.handle.net/1765/85379 |