Abstract
Collaborative filtering algorithm is the most widely used and most successful recommendation algorithm currently, however, the existing algorithm has the problem of data sparsity, cold start and scalability problems etc. The corresponding improvement solution for the scalability problem has been proposed in this paper, a paralleled transform based on the Hadoop MapReduce model has been designed to improve the algorithm efficiency. Finally the Hadoop cluster recommender system for the improved hybrid collaborative filtering algorithm based on Genetic algorithm optimization has been designed, and the experimental performance verification has been improved greatly compared than the traditional filtering algorithm.
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