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초록
영어
Most existing calculations of similarities suffer from data sparsity and poor prediction quality problems. For this issue, we proposed a similarity measurement algorithm based on entropy. The entropy is computed by the difference of two users’ ratings, and we also consider the size of their common rated items, the size is bigger, the weight of their similarity is higher. Experiments show that the algorithm effectively solves the problem of the inaccuracy of similarities in data sparsity or small size neighborhood environments, and outperforms other state-of-the-art CF algorithms and it is more robust against data sparsity.
목차
Abstract
1. Introduction
2. Existing Solutions
3. Similarity Measuring Technique based on Entropy
3.1 Motivation of this Proposal
3.2 Algorithm Design
4. Experiment Design and Discussion
4.1 Experimental Data
4.2 Experimental Evaluation Strategy
4.3 Experimental Results and Discussion
5. Conclusion
References
1. Introduction
2. Existing Solutions
3. Similarity Measuring Technique based on Entropy
3.1 Motivation of this Proposal
3.2 Algorithm Design
4. Experiment Design and Discussion
4.1 Experimental Data
4.2 Experimental Evaluation Strategy
4.3 Experimental Results and Discussion
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보