Content-based news recommendation is traditionally performed using the cosine similarity and TF-IDF weighting scheme for terms occurring in news messages and user pro- files. 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 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.

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23rd International Conference on World Wide Web, WWW 2014
Erasmus University Rotterdam

Hogenboom, F., Capelle, M., Moerland, M., & Frasincar, F. (2014). Bing-SF-IDF+: Semantics-driven news recommendation. In WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web (pp. 291–292). doi:10.1145/2567948.2577310