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http://dx.doi.org/10.6109/jkiice.2018.22.1.17

Design a Method Enhancing Recommendation Accuracy Using Trust Cluster from Large and Complex Information  

Noh, Giseop (Republic of Korea Air Academy)
Oh, Hayoung (Department of DASAN University Colleage, Ajou University)
Lee, Jaehoon (Department of Computer Science, Seoul National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.1, 2018 , pp. 17-25 More about this Journal
Abstract
Recently, with the development of ICT technology and the rapid spread of smart devices, a huge amount of information is being generated. The recommendation system has helped the informant to judge the information from the information overload, and it has become a solution for the information provider to increase the profit of the company and the publicity effect of the company. Recommendation systems can be implemented in various approaches, but social information is presented as a way to improve performance. However, no research has been done to utilize trust cluster information among users in the recommendation system. In this paper, we propose a method to improve the performance of the recommendation system by using the influence between the intra-cluster objects and the information between the trustor-trustee in the cluster generated in the online review. Experiments using the proposed method and real data have confirmed that the prediction accuracy is improved than the existing methods.
Keywords
Recommender system; Online social relation; Social relation cluster; Social network analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, et al., "Personalized recommendation of social software items based on social relations," in Proceedings of the third ACM conference on Recommender systems, pp. 53-60, 2009.
2 S. Feil, M. Kretzer, K. Werder, and A. Maedche, "Using gamification to tackle the cold-start problem in recommender systems," in Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, pp. 253-256, 2016.
3 W. X. Zhao, S. Li, Y. He, E. Y. Chang, J.-R. Wen, and X. Li, "Connecting social media to e-commerce: Cold-start product recommendation using microblogging information," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 5, pp. 1147-1159, May 2016.   DOI
4 M. Jamali and M. Ester, "A matrix factorization technique with trust propagation for recommendation in social networks," in Proceedings of the fourth ACM conference on Recommender systems, pp. 135-142, 2010.
5 H. Ma, H. Yang, M. R. Lyu, and I. King, "Sorec: social recommendation using probabilistic matrix factorization," in Proceedings of the 17th ACM conference on Information and knowledge management, pp. 931-940, 2008.
6 X. Yang, H. Steck, and Y. Liu, "Circle-based recommendation in online social networks," in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1267-1275, 2012.
7 H. Li, D. Wu, W. Tang, and N. Mamoulis, "Overlapping community regularization for rating prediction in social recommender systems," in Proceedings of the 9th ACM Conference on Recommender Systems, pp. 27-34, 2015.
8 Epinions. (2017, July 7). Epinions, Unbiased Reviews by Real People. Available: http://www.epinions.com/.
9 J. D. Rennie and N. Srebro, "Fast maximum margin matrix factorization for collaborative prediction," in Proceedings of the 22nd international conference on Machine learning, pp. 713-719, 2005.
10 Y. Koren, "Factor in the neighbors: Scalable and accurate collaborative filtering," ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 4, no. 1, pp. 1-24, Jan. 2010.
11 H. Ma, I. King, and M. R. Lyu, "Effective missing data prediction for collaborative filtering," in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 39-46, 2007.
12 H. Yi and F. Zhang, "Robust recommendation method based on suspicious users measurement and multidimensional trust," Journal of Intelligent Information Systems, vol. 46, no. 2, pp. 349-367, Apr. 2016.   DOI
13 I.-Y. Jeong, X. Yang, and H.-K. Jung, "A Study on Movies Recommendation System of Hybrid Filtering-Based," Journal of the Korea Institute of Information and Communication Engineering, vol. 19, no. 1, pp. 113-118, Jan. 2015.   DOI
14 J-H Kim, J-H Kim, S-W Jeong, S-J Kang, "Multi-level Song Recommendation System based on SNS Posts and Sentiment Analysis," Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, vol.7, no.3, pp. 283-290, Mar. 2017.