%0 Journal Article
	%A Kyoung-Jae Kim and  Hyunchul Ahn
	%D 2012
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 64, 2012
	%T Hybrid Recommender Systems using Social Network Analysis
	%U https://publications.waset.org/pdf/1249
	%V 64
	%X This study proposes novel hybrid social network analysis and collaborative filtering approach to enhance the performance of recommender systems. The proposed model selects subgroups of users in Internet community through social network analysis (SNA), and then performs clustering analysis using the information about subgroups. Finally, it makes recommendations using cluster-indexing CF based on the clustering results. This study tries to use the cores in subgroups as an initial seed for a conventional clustering algorithm. This model chooses five cores which have the highest value of degree centrality from SNA, and then performs clustering analysis by using the cores as initial centroids (cluster centers). Then, the model amplifies the impact of friends in social network in the process of cluster-indexing CF.

	%P 515 - 518