원문정보
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초록
영어
Memory-based CF algorithms have the weakness of low real-time ability and scalability. For these issues, a SVD-based K-means clustering CF algorithm is proposed. Traditional clustering-based CF algorithms have low recommendation precision because of data sparsity. So we first fill the missing ratings by SVD prediction, and then implement k-means clustering in the filled matix. This algorithm overcomse the data sparsity issue via SVD and keep the advantage of clustering, such as good real-time ability and scalability. Experiments results show that this algorithm outperforms Pearson CF, svd CF and k-means CF.
목차
Abstract
1. Introduction
2. K-means Collaborative Filtering Algorithm based on SVD
2.1 Singular Value Decomposition
2.2 K-means Clustering
3. Design of the Algorithm
4. Experiment Design and Discussion
4.1 Experimental Data
4.2 Experimental Results
5. Conclusion
References
1. Introduction
2. K-means Collaborative Filtering Algorithm based on SVD
2.1 Singular Value Decomposition
2.2 K-means Clustering
3. Design of the Algorithm
4. Experiment Design and Discussion
4.1 Experimental Data
4.2 Experimental Results
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보