earticle

영어

This study proposes novel recommender systems which collect qualitative and emotional information for recommendation engine in order to mitigate the limitations of typical collaborative filtering (CF) algorithm. 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 amplify the impact of friends in social network in the process of cluster-indexing CF. The experimental results show the proposed model outperforms conventional CF and other comparative models with statistical significance.