Earticle

초록

영어

Collaborative Filtering is of particular interest because its recommendations are based on the preferences of similar users. This allows us to overcome several key limitations. This paper explains the need for collaborative filtering, its benefits and related challenges. We have investigated several variations and their performance under a variety of circumstances. We also explored the implications of these results when weighing K Nearest Neighbor algorithm for implementation. Based on the relationship of individuals, putting forward a new incremental learning collaborative filtering recommendation system, discovery it is a better way to acquire optimum results.

목차

Abstract
 1. Introduction
 2. Collaborative Filtering
  2.1 Representation
  2.2 Generation of Recommendation
 3. K Nearest Neighbor Algorithm in CF
  3.1 KNN for Density Estimation
  3.2 KNN Classification
 4. Our Method of Similarity Analysis by Using KNN Algorithm
 5. Conclusions and Future Work
 Acknowledgments
 References

키워드

저자

• Yingchun Hou [ School of Computer Science and Technology, Xidian University, Xi’an 710071, P. R.China, Department of Computer Technology, Shangqiu Polytechnic, Shangqiu 476000, P. R.China, ]
• Hui Xie [ School of Mathematics & Computer Science, Jiangxi Science & Technology Normal University, Nanchang 330038, P. R. China ]
• Jianfeng Ma [ School of Computer Science and Technology, Xidian University, Xi’an 710071, P. R.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