User Modeling, Adaptation, and Personalization
User Modeling, Adaptation, and Personalization
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DOI：
10.1007/978-3-642-38844-6
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
V. Dimitrova;T. Kuflik;David N. Chin;F. Ricci;Peter Dolog;G. Houben
中科院分区：
文献类型：
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作者：
V. Dimitrova;T. Kuflik;David N. Chin;F. Ricci;Peter Dolog;G. Houben
博物馆的在线藏品通常很难访问，因为艺术品缺乏适当的注释。我们开发了一个框架，支持人群中的专家添加高质量的注释。本论文重点研究将专家与艺术品进行注释相匹配的搜索策略。我们的方法使用显式语义来对集合项的属性、旨在多样化的基于内容的过滤以及结果的信任感知排名之间的关系进行建模。
Online collections of museums are often hard to access, because the artworks lack appropriate annotations. We develop a framework that supports niches of experts in the crowd in adding annotation of high quality. This thesis focuses on search strategies that match experts with artworks to annotate. Our approach uses explicit semantics for modeling the relations between the properties of the collection items, content-based filtering aimed at diversification, and trust-aware ranking of the results.