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Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

机译:基于混合含量的混合含量和协作滤波方法在推荐系统中具有Apache Spark的推荐系统

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Collaborative Filtering as a popular method that used for recommendation system. Improvisation is done in purpose of improving the accuracy of the recommendation. A way to do this is to combine with content based method. But the hybrid method has a lack in terms of scalability. The main aim of this research is to solve problem that faced by recommendation system with hybrid collaborative filtering and content based method by applying parallelization on the Apache Spark platform.Based on the test results, the value of hybrid collaborative filtering method and content based on Apache Spark cluster with 2 node worker is 1,003 which then increased to 2,913 on cluster having 4 node worker. The speedup got more increased to 5,85 on the cluster that containing 7 node worker.
机译:协同过滤作为一种用于推荐系统的流行方法。 即兴创作是为了提高建议书的准确性。 这样做的方法是与基于内容的方法组合。 但混合方法缺乏可扩展性。 该研究的主要目的是解决推荐系统面临的问题,通过在Apache Spark平台上应用了并行化的混合协作滤波和基于内容的方法。基于测试结果,基于Apache的混合协作滤波方法和内容的值 带有2个节点工作人员的Spark群集是1,003,然后在具有4个节点工人的群集中增加到2,913。 包含7个节点工作人员的群集中的加速度更大增加到5,85。

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