By Topic

Efficient top-k support documents for expert search using relationship in a social network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Ji-Meng Chen ; Coll. of Inf. Technol. Sci., Nankai Univ., Tianjin, China ; Jie Liu ; Ya-Lou Huang ; Min Lu

Searching experts for helping make decision in an organization is an effective solution. Traditional approaches of expert search only use the expertise information of a single expert and ignore relationship between persons. Recently some research shows that relationship between persons is also helpful. However, most approaches cost much time and energy to establish expertise profiles for all experts and extract varies of social relationship between them. In this paper, we propose an approach which can not only efficiently collect expertise information of each expert through top-k support documents, but also use effective co-occurrence relationship between expert candidates to rank the target experts. The use of co-occurrence relationship aims to quickly build a social network. And it also enhances reliability of relevance between expert candidates and a given topic, and improves accuracy of recommended experts. Experimental results on W3C collection show that our approach outperforms the baseline approaches.

Published in:

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on  (Volume:4 )

Date of Conference:

10-13 July 2011