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.
This work is partially supported by the 863 Program (No. 2012AA011101) and the Natural Science Foundation of China (No. 61272344 and 61202233).
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Rao, J., Jia, A., Feng, Y., Zhao, D. (2013). Personalized News Recommendation Using Ontologies Harvested from the Web. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_79
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DOI: https://doi.org/10.1007/978-3-642-38562-9_79
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