원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.8 No.3
2015.03
pp.133-144
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Collaborative Filtering is of particular interest because its recommendations are based on the preferences of similar users. This allows us to overcome several key limitations. This paper explains the need for collaborative filtering, its benefits and related challenges. We have investigated several variations and their performance under a variety of circumstances. We also explored the implications of these results when weighing K Nearest Neighbor algorithm for implementation. Based on the relationship of individuals, putting forward a new incremental learning collaborative filtering recommendation system, discovery it is a better way to acquire optimum results.
목차
Abstract
1. Introduction
2. Collaborative Filtering
2.1 Representation
2.2 Generation of Recommendation
3. K Nearest Neighbor Algorithm in CF
3.1 KNN for Density Estimation
3.2 KNN Classification
4. Our Method of Similarity Analysis by Using KNN Algorithm
5. Conclusions and Future Work
Acknowledgments
References
1. Introduction
2. Collaborative Filtering
2.1 Representation
2.2 Generation of Recommendation
3. K Nearest Neighbor Algorithm in CF
3.1 KNN for Density Estimation
3.2 KNN Classification
4. Our Method of Similarity Analysis by Using KNN Algorithm
5. Conclusions and Future Work
Acknowledgments
References
저자정보
참고문헌
자료제공 : 네이버학술정보