원문정보
보안공학연구지원센터(IJUNESST)
International Journal of u- and e- Service, Science and Technology
Vol.9 No.2
2016.02
pp.101-108
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
For the issue of single CF algorithm performing low recommendation precision, we propose an adaptive Adaptive-Boost.RT ensemble learning algorithm. First, the base regression predictor is formed by minimizing the error function of user’s predicting ratings via gradient descent algorithm. Then, we introduce an adaptive error parameter, which has statistical property and can be adjusted automatically by the predict error, instead of original parameter. Experiments results demonstrate that this ensemble learning algorithm can improve performance of single CF model significantly.
목차
Abstract
1. Introduction
2. Ensemble Learning Summary
2.1 Basic Conception
2.2 Individual Generation Method
3. An Improved Adaptive-Boost Collaborative Filtering Algorithm
3.1. Design of base Class Learning Algorithm
3.2. Design of the Improved Adaptive-Boost. RT Algorithm
4. Experiment Design and Analyst
4.1. Experimental Data
4.2 Experiment Design and Discussion
5. Conclusion
References
1. Introduction
2. Ensemble Learning Summary
2.1 Basic Conception
2.2 Individual Generation Method
3. An Improved Adaptive-Boost Collaborative Filtering Algorithm
3.1. Design of base Class Learning Algorithm
3.2. Design of the Improved Adaptive-Boost. RT Algorithm
4. Experiment Design and Analyst
4.1. Experimental Data
4.2 Experiment Design and Discussion
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보