Personalized news recommendation using ontologies harvested from the web

Authors:
Junyang Rao;Aixia Jia;Yansong Feng;Dongyan Zhao
Affiliations:
ICST, Peking University, China;ICST, Peking University, China;ICST, Peking University, China;ICST, Peking University, China
Venue:
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Year:
2013

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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.