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Personalcasting: Tailored Broadcast News

机译：个人广播：量身定制的广播新闻

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摘要

Broadcast news sources and newspapers provide society with the vast majority of realtime information. Unfortunately, cost efficiencies and real-time pressures demand that producers, editors, and writers select and organize content for stereotypical audiences. In this article we illustrate how content understanding, user modeling, and tailored presentation generation promise personal-casts on demand. Specifically, we report on the design and implementation of a personalized version of a broadcast news understanding system, MITRE's Broadcast News Navigator (BNN), that tracks and infers user content interests and media preferences. We report on the incorporation of Local Context Analysis to both expand the user's original query to the most related terms in the corpus, as well as to allow the user to provide interactive feedback to enhance the relevance of selected news stories. We describe an empirical study of the search for stories on ten topics from a video corpus. By personalizing both the selection of stories and the form in which they are delivered, we provide users with tailored broadcast news. This individual news personalization provides more fine-grained content tailoring than current personalized television program level recommenders and does not rely on externally provided program metadata.
机译：广播新闻来源和报纸为社会提供了绝大多数的实时信息。不幸的是，成本效率和实时压力要求制片人，编辑和作家为定型受众选择和组织内容。在本文中，我们说明了内容理解，用户建模和量身定制的演示文稿生成方式如何保证按需进行个人广播。具体来说，我们报告了广播新闻理解系统（MITRE的广播新闻导航器（BNN））的个性化版本的设计和实现，该系统跟踪并推断用户的内容兴趣和媒体偏好。我们报告结合本地上下文分析，以将用户的原始查询扩展到语料库中最相关的术语，并允许用户提供交互式反馈以增强所选新闻故事的相关性。我们描述了从视频语料库中搜索关于十个主题的故事的实证研究。通过个性化报道的选择和报道的形式，我们为用户提供量身定制的广播新闻。与当前的个性化电视节目级别推荐者相比，这种个性化的新闻个性化提供了更细粒度的内容定制，并且不依赖于外部提供的节目元数据。

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