| 基于Hadoop与Mahout的协同过滤图书推荐研究 |
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| 引用本文： | 奉国和,黄家兴.基于Hadoop与Mahout的协同过滤图书推荐研究[J].图书情报工作,2013,57(18):116-121. |
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| 作者姓名： | 奉国和 黄家兴 |
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| 作者单位： | 1. 华南师范大学经济与管理学院;
2. 中国电信广东公司网络监控维护中心 |
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| 摘 要： | 基于Hadoop开源分布式计算框架和Mahout协同过滤推荐引擎技术构建图书推荐引擎系统,并利用云模型和Pearson系数对传统协同过滤推荐算法进行改进,改善传统单机推荐算法在高维稀疏矩阵上进行运算所导致的系统性能不佳及推荐结果不准确的问题。利用实验对分布式推荐平台的整体性能及改善后的协同过滤推荐算法进行测试评估,发现当虚拟机节点不断增加时,协同过滤推荐引擎的计算时间不断减少,这表明推荐引擎系统的总体性能较传统单机推荐引擎得到提升;利用MAE分别对原始协同过滤推荐效果和改进后的推荐算法进行测评,发现改进后的推荐引擎算法的推荐准确率较改进前提高13.1%。
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| 关 键 词： | 图书推荐 Hadoop Mahout 推荐引擎 协同过滤 |
| 收稿时间： | 2013-08-05 |
Research on Collaborative Filtering Book Recommendation Based on Hadoop and Mahout |
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| Feng Guohe,Huang Jiaxing.Research on Collaborative Filtering Book Recommendation Based on Hadoop and Mahout[J].Library and Information Service,2013,57(18):116-121. |
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| Authors: | Feng Guohe Huang Jiaxing |
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| Institution: | 1. School of Economics & Management, South China Normal University, Guangzhou 510006;
2. Center of network control, China Telecom Guangdong Branch, Guangzhou 510080 |
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| Abstract: | Firstly, this paper builds a book recommendation engine system based on the Hadoop open source distributed computing framework and mahout collaborative filtering recommendation engine technology. Then it takes advantage of the cloud model and Pearson coefficient to improve the traditional collaborative filtering recommendation algorithm, and resolves the problems of poor system performance and recommendation results inaccurate of traditional stand-alone recommendation algorithm in high-dimensional sparse matrix operations. Thirdly, it experiments and evaluates the overall performance of the distributed recommendation platform and the improved collaborative filtering algorithm. It finds that: (1) when the virtual machine nodes are increasing, the computation time of collaborative filtering recommendation engine is declining in the experimental tests, which shows that the overall performance of the system has been improved. (2) it improves the mahout original collaborative filtering recommendation engine with the Pearson coefficient and evaluates the recommended effect with MAE indices of the original collaborative filtering recommendation algorithm, which finds the recommendation accuracy rate increases 13.1% and the subjectivity differences of user ratings have great impact on the recommendation accuracy. |
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| Keywords: | book recommendation Hadoop Mahout recommendation engine collaborative filtering |
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