MapReduce based computation of the diffusion method in recommender systems

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 .