DocumentCode :
62865
Title :
Dynamic Latent Expertise Mining in Social Networks
Author :
Ofek, N. ; Shabtai, Asaf
Author_Institution :
Dept. of Inf. Syst. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
Volume :
18
Issue :
5
fYear :
2014
fDate :
Sept.-Oct. 2014
Firstpage :
20
Lastpage :
27
Abstract :
With more individuals using social networks as well as a wider range of activities available by these platforms, there is a growing need to develop knowledge-extraction methods. This article presents ExaMine, a system for identifying expertise within a user´s social network connections. During the learning phase, the system mines the activities associated with each connection to generate profiles. When the user browses the Web, the system retrieves an ordered list of connections for any viewed webpage. It then uses a classification process to identify these connections as experts on the webpage´s dynamic topics of the webpage according to a classification process.
Keywords :
classification; data mining; information retrieval; learning (artificial intelligence); online front-ends; social networking (online); ExaMine system; Web browsing; Webpage dynamic topics; classification process; dynamic latent expertise mining; knowledge-extraction methods; learning phase; retrieval; social network; user connections; Data mining; Data models; Facebook; Information retrieval; Internet; Social network services; Web pages; data mining; information retrieval; social networks;
fLanguage :
English
Journal_Title :
Internet Computing, IEEE
Publisher :
ieee
ISSN :
1089-7801
Type :
jour
DOI :
10.1109/MIC.2014.83
Filename :
6840827
Link To Document :
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