Earticle

초록

영어

In mobile network, the mobile user has the strict requirement for the performance of accessing the information. In order to provide the personalized service for mobile user timely and accurately, an online learning model of mobile user preference based on context quantification is proposed. In the model, a context quantification method is proposed, which can enhance the accuracy of learned mobile user preference; and the sliding window and the online extreme leaning machine (O-ELM) are introduced to realize the online learning. Firstly, it needs to judge whether the mobile user preference is affected by the context through analyzing the mobile user behaviors. Secondly, the context is quantified according to the context relevancy and the context similarity. And then, the sliding window is employed to select the samples that need to be learned when updating the mobile user preference. Finally, O-ELM is employed to learn the mobile user preference. The experimental results show that the proposed method surpasses the existing methods in the performance.

목차

Abstract
 1. Introduction
 2. Overview of Mobile User Preference Learning
 3. The Context Quantification
  3.1. The determination of context
  3.2. The Context Relevancy
  3.3. The Context Similarity
  3.4. The Step of the Context Quantification
  3.5. The Rational Analysis of the Proposed Quantification
 4. The Proposed Online Learning Model
  4.1. The Sliding Window
  4.2. The O-ELM
  4.3. The Construction of the Model
 5. The Experimental Results And Analysis
  5.1. The Data Set
  5.2. Evaluation
  5.3. The Experimental Step
  5.4. The Experimental Results and Analysis
 6. Conclusion
 Acknowledgement
 References

키워드

저자

• Yancui Shi [ Institute of Computer Science and Information Engineering Tianjin University of Science & Technology, Tianjin China ]
• Congcong Xiong [ Institute of Computer Science and Information Engineering Tianjin University of Science & Technology, Tianjin China ]
• Jucheng Yang [ Institute of Computer Science and Information Engineering Tianjin University of Science & Technology, Tianjin China ]
• Yarui Chen [ Institute of Computer Science and Information Engineering Tianjin University of Science & Technology, Tianjin China ]
• Jianhua Cao [ Institute of Computer Science and Information Engineering Tianjin University of Science & Technology, Tianjin 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