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MapReduce based computation of the diffusion method in recommender systems

机译:基于MapReduce的推荐系统中扩散方法的计算

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摘要

The performance of existing diffusion-based algorithms in recommender systems is still limited by the processing ability of a single computer .In order to conduct the diffusion computation on large data sets, a parallel implementation of the classic diffusion method on the MapReduce framework is proposed.At first, the diffusion computation is transformed from a summation format to a cascade matrix multiplication format , and then , a parallel matrix multiplication algorithm based on dynamic vector is proposed to reduce the CPU and I/O cost on the MapReduce framework , which can also be applied to other parallel matrix multiplication scenarios .Then, block partitioning is used to further improve the performance , while the order of matrix multiplication is also taken into consideration . Experiments on different kinds of data sets have verified the efficiency of the proposed method .
机译:推荐器系统中现有基于扩散的算法的性能仍然受到单台计算机处理能力的限制。为了对大型数据集进行扩散计算,提出了在MapReduce框架上并行实现经典扩散方法的并行实现。首先,将扩散计算从求和格式转换为级联矩阵乘法格式,然后提出一种基于动态矢量的并行矩阵乘法算法,以减少MapReduce框架上的CPU和I / O成本,这也可以块分割用于进一步提高性能,同时还考虑了矩阵乘法的顺序。通过对不同类型数据集的实验验证了该方法的有效性。

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