Earticle

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

키워드

저자

• Liu Feng [ Heilongjiang University of Technology, Jixi 158100, china, ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605