Earticle

초록

영어

As an important tool to help users filter Internet information, recommender system has played a very important role wherever in academia or in industrial area. During the past years, different recommendation approaches based on the social network have been proposed with the rapid development of online social networks. Different from the traditional ones which assume all the users are independent and identically distributed, these approaches follow the intuition that a person’s implicit or explicit social network will affect his behaviors on the Web. In this paper, on the basis of the existing work, we fuse a baseline predictor model with an improved social recommendation model and propose a social recommendation algorithm based on probability matrix factorization. The experimental result shows that our method outperforms the existing approaches in accuracy.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Problem Description
 4. Social Recommendation Algorithm
  4.1. BPMF Model
  4.2. ISMF Model
  4.3. BISMF Model
  4.4. Similarity Function
  4.5. Complexity Analysis
 5. Results and Analysis
  5.1. Experimental Environment and Data Set
  5.2. Evaluation Index
  5.3. Experimental Results
  5.4. Influence of θc 
 6. Conclusion
 Acknowledgment
 References

키워드

저자

• Wei Luo [ Faculty of Information Science and Technology, Dalian Maritime University, Dalian 116024, China, Department of Computer Science, Dalian Neusoft Institute of Information, Dalian 116626, China ]
• Zhihao Peng [ Faculty of Information Science and Technology, Dalian Maritime University, Dalian 116024, China, Department of Computer Science, Dalian Neusoft Institute of Information, Dalian 116626, China ]
• Ansheng Deng [ Faculty of Information Science and Technology, Dalian Maritime University, Dalian 116024, China ] Corresponding Author

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