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The Interplay of divrank:信誉和信息网络的多样性

DivRank: the Interplay of Prestige and Diversity in Information Networks
课程网址: http://videolectures.net/kdd2010_mei_dripd/  
主讲教师: Qiaozhu Mei
开课单位: 密歇根大学
开课时间: 2010-10-01
课程语种: 英语
中文简介:
信息网络被广泛用于表征诸如文本文档之类的数据项之间的关系。许多重要的检索和挖掘任务依赖于根据数据项在网络中的中心性或声望对数据项进行排名。除了声望之外,多样性已被认为是排名中的关键目标,旨在在排名靠前的结果中提供非冗余和高覆盖率的信息。然而,现有的基于网络的排名方法要么忽视多样性的关注,要么通过非优化的启发式处理它,通常基于贪婪的顶点选择。我们提出了一种新的排名算法DivRank,它基于信息网络中的强化随机游走。该模型以原则方式自动平衡排名靠前的顶点的声望和多样性。 DivRank不仅具有明确的优化解释,而且与数学和网络科学中的经典模型有很好的联系。我们使用三个不同网络上的经验实验以及文本摘要任务来评估DivRank。 DivRank在提升声望多样性方面优于现有的基于网络的排名方法。
课程简介: Information networks are widely used to characterize the relationships between data items such as text documents. Many important retrieval and mining tasks rely on ranking the data items based on their centrality or prestige in the network. Beyond prestige, diversity has been recognized as a crucial objective in ranking, aiming at providing a non-redundant and high coverage piece of information in the top ranked results. Nevertheless, existing network-based ranking approaches either disregard the concern of diversity, or handle it with non-optimized heuristics, usually based on greedy vertex selection. We propose a novel ranking algorithm, DivRank, based on a reinforced random walk in an information network. This model automatically balances the prestige and the diversity of the top ranked vertices in a principled way. DivRank not only has a clear optimization explanation, but also well connects to classical models in mathematics and network science. We evaluate DivRank using empirical experiments on three different networks as well as a text summarization task. DivRank outperforms existing network-based ranking methods in terms of enhancing diversity in prestige.
关 键 词: 信息网络; 排名算法; 网络科学中
课程来源: 视频讲座网
最后编审: 2019-05-11:lxf
阅读次数: 90