Browse > Article
http://dx.doi.org/10.13088/jiis.2013.19.4.001

Recommender System using Implicit Trust-enhanced Collaborative Filtering  

Kim, Kyoung-Jae (Business School, Dongguk University_Seoul)
Kim, Youngtae (Department of Management Information Systems, Graduate School, Dongguk University_Seoul)
Publication Information
Journal of Intelligence and Information Systems / v.19, no.4, 2013 , pp. 1-10 More about this Journal
Abstract
Personalization aims to provide customized contents to each user by using the user's personal preferences. In this sense, the core parts of personalization are regarded as recommendation technologies, which can recommend the proper contents or products to each user according to his/her preference. Prior studies have proposed novel recommendation technologies because they recognized the importance of recommender systems. Among several recommendation technologies, collaborative filtering (CF) has been actively studied and applied in real-world applications. The CF, however, often suffers sparsity or scalability problems. Prior research also recognized the importance of these two problems and therefore proposed many solutions. Many prior studies, however, suffered from problems, such as requiring additional time and cost for solving the limitations by utilizing additional information from other sources besides the existing user-item matrix. This study proposes a novel implicit rating approach for collaborative filtering in order to mitigate the sparsity problem as well as to enhance the performance of recommender systems. In this study, we propose the methods of reducing the sparsity problem through supplementing the user-item matrix based on the implicit rating approach, which measures the trust level among users via the existing user-item matrix. This study provides the preliminary experimental results for testing the usefulness of the proposed model.
Keywords
내재적 평가;희박성;추천시스템;협업필터링;고객관계관리;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Breese, J., D. Heckerman and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, (1998), 43-52.
2 Goldberg, K., T. Roeder, D. Gupta and C. Perkins, "Eigentaste: A constant time collaborative filtering algorithm," Information Retrieval Journal, Vol.4, No.2(2001), 133-151.   DOI
3 Papagelis, M., D. Plexousakis and T. Kutsuras, "Alleviating the sparsity problem of collaborative filtering using trust inferences," Proceeding iTrust'05 Proceedings of the Third international conference on Trust Management, (2005), 224-239.
4 Sarwar, B. M., J. A. Konstan, A. Borchers, J. Herlocker, B. Miller and J. Riedl, "Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system," Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work, (1998), 345-354.
5 Shambour, Q. and J. Lu, "A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services," International Journal of Intelligent Systems, Vol.26, No.9(2011), 814-843.   DOI   ScienceOn