Earticle

초록

영어

Collaborative filtering is the most used personalized recommendation technology. However, the traditional collaborative filtering faces the cold start problem and data sparsity, which deteriorates user experience and reduces the prediction accuracy. This paper presents a novel solution of new user problem with social network analysis (SNA) theory. First, the user relationship network is built based on the user-item rating matrix. Then all users will be divided into many different cliques according to SNA. The demographic user information of new user is used to find similar users in user attributes. Further, the candidate neighbors of new user can be obtained from cliques. Then, the preferential attachment characteristic of the free-scale network is introduced to eliminating the unsuitable neighbors. Finally, items are recommended with user-based nearest neighbor recommendation algorithm. The experiments show that the proposed approach alleviates the new user problem and improves the prediction accuracy effectively compared with other algorithms.

목차

Abstract
 1. Introduction
 2. Related work
 3. Proposed approach
  3.1. User relationship network and cliques
  3.2. User information vector model
  3.2. Select neighbors
 4. Experiments and analysis
  4.1. Dataset
  4.2. Performance evaluation
  4.3. Results and analysis
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Yang Yujie [ Institute of Computer and Information Technology, Henan Normal University, Computational Intelligence and Data Mining Engineering Technology Research Center of Colleges and Universities in Henan Province, Xinxiang Henan China ]
• Zhang Huizhi [ Institute of Computer and Information Technology, Henan Normal University, Computational Intelligence and Data Mining Engineering Technology Research Center of Colleges and Universities in Henan Province, Xinxiang Henan China ]
• Wang Xianfang [ Institute of Computer and Information Technology, Henan Normal University, Computational Intelligence and Data Mining Engineering Technology Research Center of Colleges and Universities in Henan Province, Xinxiang Henan 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