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Pervasive parallelism in data mining
2009
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a collaborative filtering (CF) solution to the Netflix Prize problem [1] based on weighted co-clustering [5] . The dataflow library we use facilitates the development of sophisticated parallel programs designed to fully utilize commodity multicore hardware, while hiding traditional difficulties such as queuing, threading,
doi:10.1145/1557019.1557140
dblp:conf/kdd/DaruruMWG09
fatcat:tnloaxmvozf7ddauyvyy4f7t3i