Earticle

초록

영어

The trust-aware recommender system (TARS) suggests the worthwhile information to the
users on the basis of trust. Existing models of TARS use personalized rating prediction
mechanisms, which can provide personalized services to each user, but they are computational
very expensive. We therefore propose an efficient global rating prediction mechanism
for TARS: we use the k-shell decomposition to find the most influential nodes in the trust
network, and use the recommendations given by these nodes to predict global ratings on
items. The experimental results verify that our proposed method can predict ratings accurately
with low computational complexity.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Our Proposed Global Rating Prediction Mechanism using k-shell Decomposition
  3.1 Degree: A Heuristic Measure on Building the Global Rating Prediction Mechanism
  3.2 K-shell: A Measure to Detect the Most Influential Users in Trust Networks
  3.3 Comparing the Effectiveness of Degree and k-Shell on TARS
 4. Conclusions and Future Works
 Acknowledgements
 References

키워드

저자

• Linshan Shen [ College of Computer Science & Technology, Harbin Engineering Univ ]
• Weiwei Yuan [ College of Computer Science & Technology, Harbin Engineering Univ ] corresponding author
• Donghai Guan [ College of Automation, Harbin Engineering Univ. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605