Abstract
In this paper, we concentrate on exploiting background knowledge to boost personalized news recommendation by capturing underlying semantic relatedness without expensive human involvement. We propose an Ontology Based Similarity Model (OBSM) to calculate the news-user similarity through collaboratively built ontological structures and compare our approach with other ontology-based baselines on both English and Chinese data sets. Our experimental results show that OBSM outperforms other baselines by a large margin. © 2013 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Rao, J., Jia, A., Feng, Y., & Zhao, D. (2013). Personalized news recommendation using ontologies harvested from the web. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7923 LNCS, pp. 781–787). Springer Verlag. https://doi.org/10.1007/978-3-642-38562-9_79
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