원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.6 No.2
2013.03
pp.27-38
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
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자료제공 : 네이버학술정보