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基于邻域的标记预测

Neighborhood-based Tag Prediction
课程网址: http://videolectures.net/eswc09_budura_nbtp/  
主讲教师: Adriana Budura
开课单位: 洛桑联邦理工学院
开课时间: 2009-07-28
课程语种: 英语
中文简介:
我们考虑了协作标记系统中的标记预测问题,用户在该系统中共享和注释Web上的资源。我们提出了一种新的方法哈姆雷特自动传播标签沿边缘的图形,这涉及到类似的文件。我们确定了标签传播的核心原则,为此我们将适当的评分模型组合到一个整体排名公式中。利用这些分数,我们提出了一种高效的Top-K标记选择算法,通过仔细检查文档图中的邻居来推断额外的标记。使用真实数据的实验证明了我们的方法在标签稀少的大规模环境中的可行性。
课程简介: We consider the problem of tag prediction in collaborative tagging systems where users share and annotate resources on the Web. We put forward HAMLET, a novel approach to automatically propagate tags along the edges of a graph which relates similar documents. We identify the core principles underlying tag propagation for which we derive suitable scoring models combined in one overall ranking formula. Leveraging these scores, we present an efficient top-k tag selection algorithm that infers additional tags by carefully inspecting neighbors in the document graph. Experiments using real-world data demonstrate the viability of our approach in large-scale environments where tags are scarce.
关 键 词: 协作标记系统; 互联网; 自动传播系统
课程来源: 视频讲座网
最后编审: 2019-12-20:lxf
阅读次数: 35