원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.8 No1
2013.01
pp.71-84
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보