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Title: PersoNews: A Personalized News Reader Enhanced by Machine Learning and Semantic Filtering
Author(s): E. Banos, I. Katakis, N. Bassiliades, G. Tsoumakas, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (8 pages).
Keywords: News Filtering News Classification, Text Mining, Text Classification, Personalization, Ontology, RSS Aggregator, ..
Appeared in: 5th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE 2006), R. Meersman, Z. Tari (Ed.), Springer-Verlag, pp. 975-982, Montpellier, France, 2006.
Abstract: In this paper, we present a web-based, machine-learning enhanced news reader (PersoNews). The main advantages of PersoNews are the aggregation of many different news sources, machine learning filtering offering personalization not only per user but also for every feed a user is subscribed to, and finally the ability for every user to watch a more abstracted topic of interest by employing a simple form of semantic filtering through a taxonomy of topics. (URL: http://news.csd.auth.gr).
See also : Springer Link


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20 Antoine Boutet, “Decentralizing news personalization systems”, PhD Thesis, Universite De Rennes 1, France, March 2013.
21 Asmaa Elbadrawy and George Karypis, “User-Specific Feature-based Similarity Models for Top-n Recommendation of New Items”, ACM Transactions on Intelligent Systems and Technology, Vol. 9, No. 4, Article 39, Publication date: March 2013.
22 Naoto Yoshida, Miyuki Yano, and Tomoko Yonezawa. 2014. Personal and interactive newscaster agent based on estimation of user's understanding. In Proceedings of the second international conference on Human-agent interaction (HAI '14). ACM, New York, NY, USA, 45-50.
23 Asmaa Elbadrawy and George Karypis, “Feature-based Similarity Models for Top-n Recommendation of New Items”, Technical Report, TR 14-016, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA, July 2014.
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