Earticle

초록

영어

Today, getting an exact result from the huge raw data on the internet is not an easy task. Utilizing search engines is a good way, but still there is a need for a tool for personalization and having the adaptive capability according to the users’ interests. In this paper, we propose a new algorithm which uses the positive and negative feedback from the user for filtering the information. We will use ART1 classifier to generate the dynamic profile of the users and utilize the genetic algorithm to make the most suitable query to give a better result according to user’s criteria search.

목차

Abstract
 1. Introduction
 2. User Profile Modeling
 3. Adaptive Response Theory Neural networks (ART1)
 4. Information Filtering
 5. Explicit and Implicit Learning
 6. Using Genetic Algorithm For Producing Efficient Queries
 7. Latent Semantic Analysis
 8. Optimized LSA
 9. TF-IDF Vector Representation
 10. Fitness Function
 11. Evaluation Results
 12. Conclusion
 References

키워드

저자

• Mahdi Bazarganigilani [ Charles Sturt University, Australia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605