Earticle

초록

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.

목차

Abstract
 Introduction
 Prior Studies
 Cluster-indexing CF with SNA
 Experimental Data and Design
  Experimental Data
  Experimental Design
  Experimental Results
 Concluding Remarks
 References

저자

• Kyoung-jae Kim [ Department of Management Information Systems, Dongguk University ]
• Hyunchul Ahn [ School of Management Information Systems, Kookmin University ]

참고문헌