원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.2 No.1
2009.01
pp.91-102
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보