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An adaptive personalized news dissemination system

机译：自适应个性化新闻发布系统

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摘要

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 .
机译：随着Word万维网的爆炸性增长，信息过载已成为至关重要的问题。在像Web这样的数据贫乏的信息贫乏的环境中，从成千上万的几乎毫无价值的数据中区分有用或期望的信息成为一项繁琐的任务。文献中充分讨论了机器学习在解决此问题中的作用，但是很少有系统可供公众使用。在这项工作中，我们通过实现基于Web的新闻阅读器，并通过将其动态设计为动态内容个性化而专门设计的机器学习框架进行了增强，将理论与实践相结合。这样，我们就有机会检查适用性和实现问题，并讨论机器学习方法对现实世界文本流进行分类的有效性。我们名为PersoNews的系统的主要功能是：（a）提供其内容的RSS版本的许多不同新闻源的汇总；（b）增量过滤，不仅针对每个用户而且针对每个提要提供内容的动态个性化用户已订阅，并且（c）通过筛选主题分类法，每个用户都可以观看更抽象的主题。 PersoNews可在WWW上免费公开使用。

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