| 基于Hadoop的协同过滤并行化算法 |
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| 引用本文： | 曹霞,谢颖华. 基于Hadoop的协同过滤并行化算法[J]. 计算机系统应用, 2018, 27(5): 166-170 |
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| 作者姓名： | 曹霞 谢颖华 |
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| 作者单位： | 东华大学 信息科学与技术学院, 上海 201620,东华大学 信息科学与技术学院, 上海 201620 |
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| 摘 要： | 在针对大数据的迅速增长，为了改善协同过滤算法的推荐效率，使得推荐精度越来越高，提出基于Hadoop平台的协同过滤并行化算法，将传统的基于用户的协同过滤在Hadoop平台下进行MapReduce编程模型，实现并行化.通过利用MovieLens公用数据集对改进前后的算法对比，验证了并行化的协同过滤效率更高，也更加适合大规模数据的推荐.
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| 关 键 词： | 协同过滤 Hadoop 并行化 MapReduce |
| 收稿时间： | 2017-09-01 |
| 修稿时间： | 2017-09-20 |
Parallel Algorithm of Collaborative Filtering Based on Hadoop |
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| CAO Xia and XIE Ying-Hua. Parallel Algorithm of Collaborative Filtering Based on Hadoop[J]. Computer Systems& Applications, 2018, 27(5): 166-170 |
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| Authors: | CAO Xia and XIE Ying-Hua |
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| Affiliation: | College of Information Science and Technology, Donghua University, Shanghai 201620, China and College of Information Science and Technology, Donghua University, Shanghai 201620, China |
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| Abstract: | In order to improve the recommendation efficiency of collaborative filtering algorithm, this study proposes a collaborative filtering parallelization algorithm based on Hadoop platform. The traditional user-based collaborative filtering is carried out under Hadoop platform for MapReduce Programming model, to achieve parallelization. By using the MovieLens common data set to improve the comparison before and after the algorithm, verify that the parallel collaborative filtering efficiency is higher, and also more suitable for large-scale data recommendation. |
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| Keywords: | collaborative filtering Hadoop clustering analysis MapReduce |
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