Earticle

초록

영어

Memory-based CF algorithms have the weakness of low real-time ability and scalability. For these issues, a SVD-based K-means clustering CF algorithm is proposed. Traditional clustering-based CF algorithms have low recommendation precision because of data sparsity. So we first fill the missing ratings by SVD prediction, and then implement k-means clustering in the filled matix. This algorithm overcomse the data sparsity issue via SVD and keep the advantage of clustering, such as good real-time ability and scalability. Experiments results show that this algorithm outperforms Pearson CF, svd CF and k-means CF.

목차

Abstract
 1. Introduction
 2. K-means Collaborative Filtering Algorithm based on SVD
  2.1 Singular Value Decomposition
  2.2 K-means Clustering
 3. Design of the Algorithm
 4. Experiment Design and Discussion
  4.1 Experimental Data
  4.2 Experimental Results
 5. Conclusion
 References

키워드

저자

• Lihua Tian [ College of geoexploration science and technology Jilin University, Changchun 130012, china, College of optical and electronical information,Changchun university of science and technology, Changchun 130012, china ]
• Liguo Han [ College of geoexploration science and technology Jilin University, Changchun 130012, china ]
• Junhua Yue [ Jilin Jianzhu University, Changchun 130012, china ]

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