Earticle

초록

영어

We predict accuracy of user’s preferences by using memory-based collaborative filtering algorithm in recommender system, and then analyze the results through the EDA approach. The possibilities are presented that prediction accuracy can be evaluated before prediction process by analyzing the results. The classification functions using the generative probability of specific ratings are made, and users are classified by using the classification functions. The prediction accuracies of each classified group are analyzed through statistical tests. The method of setting the Error Bound of users who have high probabilities in low prediction accuracy will be presented.

목차

Abstract
 1. Introduction
 2. Recommender system
  2.1. Collaborative filtering
  2.2. Neighbor selection
  2.3. Similarity weight
 3. Algorithm
  3.1. NBCFA
  3.2. Evaluation Metric
 4. Pre-evaluation
 5. Methodology
  5.1. Experimental dataset
  5.2. Error fence
  5.3. Experiment steps
 6. Experiment and results
  6.1. Relationship between MAE and SD
  6.2. Relationship between MAE and classification functions
  6.3. Relationship between classification functions and SD
  6.4. Relationship between MAE and ‘and’ condition
  6.5. Relationship between MAE and ‘or’ condition
 7. Conclusions
 8. References

저자

• Uk-Pyo Han [ Department of Computer Science, Kangwon National University, Korea ]
• Gil-Mo Yang [ School of Computer IT Engineering, Kangnung National University, Korea ]
• Jae-Soo Yoo [ School of Electrical & Computer Engineering, Chungbuk National University, Korea ]
• Young-Jun Chung [ Department of Computer Science, Kangwon National University, Korea ]
• Hee-Choon Lee [ Department of Computer Data Information, sangji University, Korea ]

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