Learning to rank domain experts in microblogging by combining text and non-text features

Qi, L; Huang, Y; Li, L; Xu, G

Learning to rank domain experts in microblogging by combining text and non-text features

Qi, L Huang, Y Li, L Xu, G

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Publication Type:: Conference Proceeding
Citation:: 2015 International Conference on Behavioral, Economic and Socio-Cultural Computing, BESC 2015, 2015, pp. 28 - 31
Issue Date:: 2015-12-24

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Field	Value	Language
dc.contributor.author	Qi, L	en_US
dc.contributor.author	Huang, Y	en_US
dc.contributor.author	Li, L	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2015-12-24	en_US
dc.identifier.citation	2015 International Conference on Behavioral, Economic and Socio-Cultural Computing, BESC 2015, 2015, pp. 28 - 31	en_US
dc.identifier.isbn	9781467387835	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41840
dc.description.abstract	© 2015 IEEE. Currently microblog search engines have the function to find related users according to input topic keywords. Traditional approaches rank users by their authentication information or their self descriptions (introductions or labels).However, many users may not publish the posts closely related to their certification profile. In this paper, we study the problem of identifying domain-dependent influential users (or topic experts). We propose to fuse of non-text features and text features to analysis the influence of the users. In addition we compare three kinds of sorting methods, i.e., order-based rank aggregation, greedy selection based rank aggregation, SVM Rank method. Our experimental results show that the highest precision is achieved by SVM rank method.	en_US
dc.relation.ispartof	2015 International Conference on Behavioral, Economic and Socio-Cultural Computing, BESC 2015	en_US
dc.relation.isbasedon	10.1109/BESC.2015.7365953	en_US
dc.title	Learning to rank domain experts in microblogging by combining text and non-text features	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. Currently microblog search engines have the function to find related users according to input topic keywords. Traditional approaches rank users by their authentication information or their self descriptions (introductions or labels).However, many users may not publish the posts closely related to their certification profile. In this paper, we study the problem of identifying domain-dependent influential users (or topic experts). We propose to fuse of non-text features and text features to analysis the influence of the users. In addition we compare three kinds of sorting methods, i.e., order-based rank aggregation, greedy selection based rank aggregation, SVM Rank method. Our experimental results show that the highest precision is achieved by SVM rank method.

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http://hdl.handle.net/10453/41840