Title :
Improving Matching Process in Social Network
Author :
Chen, Lin ; Nayak, Richi ; Xu, Yue
Author_Institution :
Comput. Sci. Discipline, Queensland Univ. of Technol., Brisbane, QLD, Australia
Abstract :
Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double.
Keywords :
data mining; pattern clustering; social networking (online); SimRank; clustering method; matching process; online dating network; recommendation method; social network; SimRank; clustering; online dating;
Conference_Titel :
Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9244-2
Electronic_ISBN :
978-0-7695-4257-7
DOI :
10.1109/ICDMW.2010.41
