A hybrid recommendation algorithm based on Hadoop

Recommender system has been widely used and collaborative filtering algorithm is the most widely used algorithm in recommender system. As scale of recommender system continues to expand, the number of users and items of recommender system is growing exponentially. As a result, the single-node machine implementing these algorithms is time-consuming and unable to meet the computing needs of large data sets. To improve the performance, we proposed a distributed collaborative filtering recommendation algorithm combining k-means and slope one on Hadoop. Apache Hadoop is an open-source organization's distributed computing framework. In this paper, the former hybrid recommendation algorithm was designed to parallel on MapReduce framework. The experiments were applied to the MovieLens dataset to exploit the benefits of our parallel algorithm. The experimental results present that our algorithm improves the performance.

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