DocumentCode :
660804
Title :
Friendship Prediction on Social Network Users
Author :
Kuan-Hsi Chen ; Liang, Tsorng-Juu
Author_Institution :
Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fYear :
2013
fDate :
8-14 Sept. 2013
Firstpage :
379
Lastpage :
384
Abstract :
Undoubtedly friendship is one of key factors which keep social networking service users active and the whole community expanding. Hence, predicting friendships becomes an indispensable service provided by the platforms like Plurk, Twitter and Facebook. In this study, an empirical prediction resolution is presented by taking into account the interactions among Plurk users in Taiwan. Both response links and content information extracted from the interaction corpus are used as features in the implementation of the vector space machine based prediction. Experimental results show that the presented approach outperforms those bag-of-word based methods presented in previous studies.
Keywords :
content-based retrieval; feature extraction; social networking (online); social sciences computing; support vector machines; user interfaces; Facebook; Plurk; Plurk user interactions; SVM model; Taiwan; Twitter; bag-of-word based methods; content information extraction; empirical prediction resolution; friendship prediction; interaction corpus; link prediction; social network users; support vector machine; vector space machine based prediction; Accuracy; Equations; Feature extraction; Message systems; Predictive models; Social network services; Support vector machines; friendship; interaction; link prediction; social network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Social Computing (SocialCom), 2013 International Conference on
Conference_Location :
Alexandria, VA
Type :
conf
DOI :
10.1109/SocialCom.2013.59
Filename :
6693356
Link To Document :
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