원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.8 No.12
2015.12
pp.171-180
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Aiming at the problem that the traditional Collaborative Filtering algorithm has low recommendation accuracy, in the paper, we propose a collaborative filtering recommendation algorithm based on the global trust degree integrating the direct trust information in the social networks. We first transform the local trust relationships to the global trust relationship by the rules in the trust network, and get the trust rank of all users in the trust networks; Then we use the global trust value to instead of the similarity information value as the weights of a predicted formula in the traditional collaborative recommendation algorithm, and integrate the weights to the matrix factorization-based recommendation model.
목차
Abstract
1. Introduction
2. Matrix Decomposition CF (Collaborative Filtering) Algorithm based on Global Confidence Degree
2.1. Recommendation Model
2.2. Basic Steps
2.3. The Algorithm Description
3. Experimental Analysis and Results
3.1. Experiment Data Set
3.2. Test Environment Configuration
3.3. Evaluation Index in the Experiment
3.4. Validation of Collaborative Filtering Recommendation Algorithm based on Global Confidence Degree
4. Conclusion
References
1. Introduction
2. Matrix Decomposition CF (Collaborative Filtering) Algorithm based on Global Confidence Degree
2.1. Recommendation Model
2.2. Basic Steps
2.3. The Algorithm Description
3. Experimental Analysis and Results
3.1. Experiment Data Set
3.2. Test Environment Configuration
3.3. Evaluation Index in the Experiment
3.4. Validation of Collaborative Filtering Recommendation Algorithm based on Global Confidence Degree
4. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보