원문정보
보안공학연구지원센터(IJGDC)
International Journal of Grid and Distributed Computing
Vol.9 No.7
2016.07
pp.169-176
초록
영어
The paper Proposed Item parallel collaborative filtering recommendation algorithm (IP-CF). Through designing efficient parallel algorithm, compute-extensive procedures are distributed to different processing nodes in Hadoop platform. Taking advantage of parallel computing, we accelerate the response of recommendation. The experimental results show that our proposed algorithm IP-CF is more efficient and scalable than current parallel algorithms.
목차
Abstract
1. Introduction
2. Collaborative Filtering Recommendation Algorithm based on item
3. Parallelization of Item-based Collaborative Filtering Recommendation Algorithm
4. Experiment Design and Discussion
4.1. Experimental Platform and Dataset
4.2. Measurement Method
4.3. Experimental Results and Analysis
5. Conclusion
References
1. Introduction
2. Collaborative Filtering Recommendation Algorithm based on item
3. Parallelization of Item-based Collaborative Filtering Recommendation Algorithm
4. Experiment Design and Discussion
4.1. Experimental Platform and Dataset
4.2. Measurement Method
4.3. Experimental Results and Analysis
5. Conclusion
References
저자정보
참고문헌
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