An adaptive personalized news dissemination system |
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Authors: | Ioannis Katakis Grigorios Tsoumakas Evangelos Banos Nick Bassiliades Ioannis Vlahavas |
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Affiliation: | (1) Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece |
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Abstract: | With the explosive growth of the Word Wide Web, information overload became a crucial concern. In a data-rich information-poor
environment like the Web, the discrimination of useful or desirable information out of tons of mostly worthless data became
a tedious task. The role of Machine Learning in tackling this problem is thoroughly discussed in the literature, but few systems
are available for public use. In this work, we bridge theory to practice, by implementing a web-based news reader enhanced
with a specifically designed machine learning framework for dynamic content personalization. This way, we get the chance to
examine applicability and implementation issues and discuss the effectiveness of machine learning methods for the classification
of real-world text streams. The main features of our system named PersoNews are: (a) the aggregation of many different news
sources that offer an RSS version of their content, (b) incremental filtering, offering dynamic personalization of the content
not only per user but also per each feed a user is subscribed to, and (c) the ability for every user to watch a more abstracted
topic of interest by filtering through a taxonomy of topics. PersoNews is freely available for public use on the WWW ().
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Keywords: | Personalization Text classification Concept drift Ontology News filtering Dynamic feature space |
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