Earticle

초록

영어

Most existing calculations of similarities suffer from data sparsity and poor prediction quality problems. For this issue, we proposed a similarity measurement algorithm based on entropy. The entropy is computed by the difference of two users’ ratings, and we also consider the size of their common rated items, the size is bigger, the weight of their similarity is higher. Experiments show that the algorithm effectively solves the problem of the inaccuracy of similarities in data sparsity or small size neighborhood environments, and outperforms other state-of-the-art CF algorithms and it is more robust against data sparsity.

목차

Abstract
 1. Introduction
 2. Existing Solutions
 3. Similarity Measuring Technique based on Entropy
  3.1 Motivation of this Proposal
  3.2 Algorithm Design
 4. Experiment Design and Discussion
  4.1 Experimental Data
  4.2 Experimental Evaluation Strategy
  4.3 Experimental Results and Discussion
 5. Conclusion
 References

키워드

저자

• Jingxia Guo [ Bao Tou Medical College, BaoTou 014060, china ]
• Jinggang Guo [ Inner Mongolia press and Publication Bureau, Hohhot 010050, 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