Earticle

초록

영어

Aiming at the problems that the existing model-based collaborative filtering algorithm has low recommendation accuracy and small recommendation coverage, we propose a collaborative filtering recommendation algorithm based on the trust propagation by introducing the trust information of social network to extend the matrix factorization-based recommendation model. We first design a set of trust propagation rules based on the direct trust relationships of the social network, so as to propagate the trust relationship in the social networks, and get to quantize the new trust relationship. Then we load the quantitative trust relations after the trust propagation as the trust weight into the matrix factorization-based model according to the characteristics that the matrix factorization technique can reduce the dimension of large-scale datasets.

목차

Abstract
 1. Introduction
 2. Related Definitions
 3. Collaborative Filtering Recommendation Model and Algorithm based on Trust Propagation
  3.1. Recommendation Algorithm based on Matrix Factorization
  3.2. Recommendation Algorithm based on Trust Propagation Mechanism
 4. Experimental Analysis and Results
  4.1. Experiment Data Set
  4.2 Test Environment Configuration
  4.3. Evaluation Index in the Experiment
  4.4. Validation of the Proposed Algorithm
  4.5. Validation of Parameter k’s Influence on Experiment Results
 5. Conclusion
 References

키워드

저자

• Miao Duan [ Jilin Jianzhu University, Jinlin 130118, china ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605