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. In this work, we bridge theory to prac- tice, by implementing a web-based news reader enhanced with a specifically designed machine learning framework for dynamic content personalization. The role of Machine Learning in tackling this problem is thoroughly discussed in the literature, but few systems are available for public use. In a data-rich information-poor environ- ment like the Web, the discrimination of useful or desirable information out of tons of mostly worthless data became a tedious task.
With the explosive growth of the Word Wide Web, information overload became a crucial concern.
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