DOI QR코드

DOI QR Code

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

대규모 복잡 정보에서 신뢰 클러스터를 이용한 추천 정확도 향상기법 설계

  • 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)
  • Received : 2017.08.21
  • Accepted : 2017.10.10
  • Published : 2018.01.31

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.

최근 ICT기술의 발전과 스마트 기기의 급격한 보급으로 엄청난 양의 정보가 생성되고 있다. 추천 시스템은 과도한 정보제공(information overload)으로부터 정보 수용자의 적절한 판단을 도와주고, 정보 제공자에게는 기업의 이윤과 업체홍보 효과를 증대 시킬 수 있는 해결책으로 등장하였다. 추천 시스템은 다양한 접근법으로 구현이 가능하지만, 소셜 네트워크 정보로 성능을 향상시킬 수 있는 방법으로 제시되었다. 그러나 추천 시스템 내의 사용자간에 형성되는 신뢰 클러스터의 정보를 활용하는 방안은 연구되지 못하였다. 본 논문에서는 온라인 리뷰에서 생성되는 클러스터에서 클러스터 내부 객체 간 영향성과 트러스터-트러스티 간 정보를 이용하여 추천 시스템의 성능을 향상시키는 방식을 제안하였다. 제안하는 방식을 구현하고 실제 데이터를 활용하여 실험한 결과 기존의 방식들보다 예측 정확도가 향상됨을 확인하였다.

Keywords

References

  1. 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. https://doi.org/10.1007/s10844-015-0375-2
  2. 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. https://doi.org/10.6109/jkiice.2015.19.1.113
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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. https://doi.org/10.1109/TKDE.2015.2508816
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Epinions. (2017, July 7). Epinions, Unbiased Reviews by Real People. Available: http://www.epinions.com/.
  13. 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.
  14. 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.