Topic detection and interest tracking in a dynamic online news source

Andrew J. Kurtz, Javed Mostafa
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引用次数: 14

Abstract

Digital libraries in the news domain may contain frequently updated data. Providing personalized access to such dynamic resources is an important goal. We investigate the area of filtering online dynamic news sources based on personal profiles. We experimented with an intelligent news-sifting system that tracks topic development in a dynamic online news source. Vocabulary discovery and clustering are used to expose current news topics. User interest profiles, generated from explicit and implicit feedback are used to customize the news retrieval system's interface.
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动态在线新闻源中的主题检测和兴趣跟踪
新闻领域的数字图书馆可能包含经常更新的数据。提供对这些动态资源的个性化访问是一个重要的目标。我们研究了基于个人资料过滤在线动态新闻源的领域。我们试验了一个智能新闻筛选系统,它可以跟踪动态在线新闻来源的主题发展。词汇发现和聚类用于揭示当前的新闻主题。根据显式和隐式反馈生成的用户兴趣配置文件用于定制新闻检索系统的界面。
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