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.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11914853_71.
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Banos, E., Katakis, I., Bassiliades, N., Tsoumakas, G., Vlahavas, I. (2006). PersoNews: A Personalized News Reader Enhanced by Machine Learning and Semantic Filtering. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE. OTM 2006. Lecture Notes in Computer Science, vol 4275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11914853_62
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DOI: https://doi.org/10.1007/11914853_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48287-1
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