Earticle

초록

영어

Existing recommender systems generate recommendation usually using user's information previously collected. The information reflects the user`s tastes, but it doesn`t include user`s intend at that time. So existing recommender systems sometimes generate not suitable recommendation because of difference between user`s current purpose and the information of past time. In this paper, we propose genetic recommend generating method for overcome this problem. Our method analyzes user`s real-time click-stream for grabbing user`s current intention, then uses genetic algorithm for generating appropriate recommendation. To reflect user`s real-time intention, the proposed method adapts fitness function of genetic algorithm continuously. To evaluate the proposed approach, we compare the proposed method with existing CF methods using the web-server log data collected from Internet jewelry shop. And we confirm that the proposed approach can generate more accurate recommendation then compared methods.

목차

Abstract.
 1 Introduction
 2 User Model
 3 Genetic Recommendation Generating Method
  3.1 About the Genetic Algorithm
  3.2 Chromosome Encoding
  3.3 Population
  3.4 A Fitness Function
  3.5 Genetic Operations
  3.6 Stopping Criteria
 4 Real-time Fitness Function Adaption
 5 Evaluation Environments
 6 Evaluation Results
  6.1 Comparison Results
  6.2 Measuring the Accuracy of Real-time Recommendation
 7 Conclusions and Future Work
 References

키워드

저자

• Minchul Jung [ School of Electrical and Computer Engineering, Sunkyunkwan University, ]
• Jehwan Oh [ School of Electrical and Computer Engineering, Sunkyunkwan University, ]
• Eunseok Lee [ School of Electrical and Computer Engineering, Sunkyunkwan University, ]

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