Distributed Recommendation Algorithm Based on Matrix Decomposition on MapReduce Framework

Distributed Recommendation Algorithm Based on Matrix Decomposition on MapReduce Framework
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MapReduce框架上基于矩阵分解的分布式推荐算法

DOI:
10.1007/978-3-319-25159-2_40
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发表时间:
2015-10
期刊:
Lecture Notes in Computer Science
影响因子:
--
通讯作者:
Xiaodong Feng
Xiaodong Feng
中科院分区:
其他
文献类型:
--
作者:
Sen Wu;Dan Lu;Yannan Du;Xiaodong Feng

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提出了一种基于矩阵运算的推荐算法(RAMO),该算法将协同过滤算法与基于信息网络的推荐方法相结合。RAMO利用来自不同对象的信息来提高推荐的准确性。在此基础上,利用MapReduce框架,提出了一种基于矩阵分解的分布式推荐算法DRAMD. DRAMD可以跨多个集群节点运行,以减少计算时间。在MovieLens数据集上的测试结果表明,该算法不仅具有较好的推荐效果,而且提高了计算效率。
This paper presents a recommendation algorithm based on matrix operations (RAMO), which integrates collaborative filtering algorithm with information network-based approach. RAMO exploits information from different objects to increase the recommendation accuracy. Furthermore, a distributed recommendation algorithm DRAMD is proposed based on matrix decomposition using the framework MapReduce. DRAMD can be run across multiple cluster nodes to reduce the computation time. Test results on MovieLens dataset show that the algorithms not only have better recommendation effectiveness but improve the efficiency of the computation.
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