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International Workshop on Knowledge Discovery and Data Mining (2010)
Phuket, Thailand
Jan. 9, 2010 to Jan. 10, 2010
ISBN: 978-0-7695-3923-2
pp: 478-481
Collaborative Filtering(CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.
collaborative filtering, recommender systems, cloud computing, hadoop, Map-Reduce

M. Shang and Z. Zhao, "User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop," 2010 3rd International Conference on Knowledge Discovery and Data Mining (WKDD 2010)(WKDD), Phuket, 2010, pp. 478-481.
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