DocumentCode :
255201
Title :
Semantic geolocation friend recommendation system; LinkedIn user case
Author :
Tajbakhsh, M.S. ; Solouk, V.
Author_Institution :
IT & Comput. Dept., Urmia Univ. of Technol., Urmia, Iran
fYear :
2014
fDate :
27-29 May 2014
Firstpage :
158
Lastpage :
162
Abstract :
The popularity and the success of every social network at the current information era lies on how strong the ties among the virtual community members are made. In turn, strong ties requires ever closer the members in terms of specific characteristics the social network is described. This paper introduces a recommender system for determining candidate connections with the highest potential of being new connections to a social network user. The proposed system investigates LinkedIn network and introduces a solution with further parameters as location information.
Keywords :
geographic information systems; recommender systems; social networking (online); LinkedIn network; location information; recommender system; semantic geolocation friend recommendation system; social network; strong ties; virtual community members; Analytical models; Blogs; Geology; Investment; MATLAB; Mathematical model; Ports (Computers); GeoLocation; LinkedIn; Recommender System; Social Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Knowledge Technology (IKT), 2014 6th Conference on
Conference_Location :
Shahrood
Print_ISBN :
978-1-4799-5658-6
Type :
conf
DOI :
10.1109/IKT.2014.7030351
Filename :
7030351
Link To Document :
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