Earticle

초록

영어

Recommender systems provide users with personalized suggestions for products. A key challenge is how to improve the diversity of recommendation results as much as possible, while maintaining reliably accurate suggestions. Although the bipartite graph based probabilistic spreading algorithm has its advantages of good accuracy and low computational complexity, its diversity is poor. In this paper, we introduce a cost-aware probabilistic spreading algorithm, and show how it can improve both recommendation accuracy and diversity by designing different spreading costs. Comparative experiments on widely used datasets confirm the effectiveness of the cost-aware probabilistic spreading approach in terms of accuracy, aggregate diversity and individual diversity of recommendation results. In addition, the time complexity of the proposed algorithm is also analyzed.

목차

Abstract
 1. Introduction
 2. Probabilistic Spreading
 3. Cost-Aware Probabilistic Spreading
  3.1 Model
  3.2 Spreading Probability and Spreading Cost
  3.3 Algorithmic Implementation
 4. Experiments
  4.1 Recommendation Performance Metrics
  4.2 Numerical Results
  4.3 Complexity Analysis
 5. Conclusion
 References

키워드

저자

• Guoyong Cai [ Guangxi KeyLab of Trusted Software, Guilin University of Electro. Tech., PRC ]
• Dong Zhang [ Guangxi KeyLab of Trusted Software, Guilin University of Electro. Tech., PRC ]
• Yumin Lin [ Guangxi KeyLab of Trusted Software, Guilin University of Electro. Tech., PRC ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605