Earticle

목차

Abstract
 1. Introduction
 2. Related Work
  2.1 Collaborative Filtering Algorithm based on Psychological Model
  2.2 Collaborative Filtering Algorithm Considering Time Factors
 3. Characteristic Analysis of Practical Recommender Systems in China
  3.1 Recommender Systems in E-commerce Websites
  3.2 Recommender Systems in Video and Music Websites
  3.3 Recommender Systems for Small Websites
 4. Proposed Algorithm
  4.1 Improved Measure of User Similarity
  4.2 Improved Time Weight Allocation Algorithm Adapting to User Interest Drift
  4.3 Improved Algorithm for Calculating Predicted Ratings
  4.4 Procedure of the Whole Algorithm
 5. Experiment and Evaluation
  5.1 Datasets
  5.2 Evaluation Criteria With development recommender systems, becomes an subject how efficient a system be. Evaluation methodology become independent research area. Currently, common criteria are prediction precision, coverage rate, diversity credibility. P
  5.3 Results and Discussions
 6. Conclusion
 Acknowledgments
 References

저자

• Wu Sen [ Donlinks School of Economics and Management, University of Science and Technology Beijing, China, ]
• Zhang Xiaonan [ Donlinks School of Economics and Management, University of Science and Technology Beijing, China, ]
• Du Yannan [ Donlinks School of Economics and Management, University of Science and Technology Beijing, 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