Ranking user authority with relevant knowledge categories for expert finding
Ranking user authority with relevant knowledge categories for expert finding
复制标题
根据相关知识类别对用户权限进行排名，以供专家查找
DOI：
10.1007/s11280-013-0217-5
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发表时间：
2014-09
期刊：
影响因子：
--
通讯作者：
Tian, Jilei
中科院分区：
文献类型：
--
作者：
Zhu, Hengshu;Chen, Enhong;Xiong, Hui;Cao, Huanhuan;Tian, Jilei
专家寻找目标的问题是通过对用户权限排序来确定具有特定知识类别（即知识领域）的特殊技能或知识的专家。近年来，随着知识共享社交网络的普及，这一问题变得越来越重要。虽然许多先前的研究已经检查了专家寻找的权威排名，但他们的重点是仅利用目标类别中的信息来寻找专家。目前尚不清楚如何利用目标类别相关类别中的信息来提高权威排名的质量。为此，本文提出了一种基于目标类别及相关类别权威信息的专家查找框架。沿着这条路线，我们开发了一种可扩展的方法，通过主题模型来测量类别之间的相关性，该方法考虑了基于类别相似性的内容和用户交互。此外，我们还提供了一种多类别敏感的主题链接分析方法，通过考虑目标类别和相关类别中的信息来对用户权限进行排名。最后，在验证方面，我们在从两个主要的商业问答（Q&A）网站收集的两个大规模真实数据集中评估了所提出的专家寻找框架。结果表明，该方法的性能明显优于基线方法。
The problem of expert finding targets on identifying experts with special skills or knowledge for some particular knowledge categories, i.e. knowledge domains, by ranking user authority. In recent years, this problem has become increasingly important with the popularity of knowledge sharing social networks. While many previous studies have examined authority ranking for expert finding, they have a focus on leveraging only the information in the target category for expert finding. It is not clear how to exploit the information in the relevant categories of a target category for improving the quality of authority ranking. To that end, in this paper, we propose an expert finding framework based on the authority information in the target category as well as the relevant categories. Along this line, we develop a scalable method for measuring the relevancies between categories through topic models, which takes consideration of both content and user interaction based category similarities. Also, we provide a topical link analysis approach, which is multiple-category-sensitive, for ranking user authority by considering the information in both the target category and the relevant categories. Finally, in terms of validation, we evaluate the proposed expert finding framework in two large-scale real-world data sets collected from two major commercial Question Answering (Q&A) web sites. The results show that the proposed method outperforms the baseline methods with a significant margin.
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DOI：
10.1145/882082.882093
发表时间：
2003-06
期刊：
--
影响因子：
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作者：
B. Dom;Iris Eiron;Alex Cozzi;Yi Zhang
通讯作者：
B. Dom;Iris Eiron;Alex Cozzi;Yi Zhang
DOI：
10.1145/1774088.1774266
发表时间：
2010-03
期刊：
--
影响因子：
--
作者：
Wei-Chen Kao;Duen-Ren Liu;Shi Wang
通讯作者：
Wei-Chen Kao;Duen-Ren Liu;Shi Wang
DOI：
10.1073/pnas.0307752101
发表时间：
2004-04-06
影响因子：
11.1
作者：
Griffiths, TL;Steyvers, M
通讯作者：
Steyvers, M
影响因子：
22.7
作者：
SALTON, G;WONG, A;YANG, CS
通讯作者：
YANG, CS
DOI：
10.1145/1871437.1871467
发表时间：
2010-10
期刊：
Proceedings of the 19th ACM international conference on Information and knowledge management
影响因子：
--
作者：
Lu Liu;Jie Tang;Jiawei Han;Meng Jiang;Shiqiang Yang
通讯作者：
Lu Liu;Jie Tang;Jiawei Han;Meng Jiang;Shiqiang Yang