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《Journal of Shaanxi University of Technology(Natural Science Edition)》 2016-06
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ALS-based matrix factorization recommendation algorithm with similar user index

SHENG Wei;YU Ying;WANG Bao-yun;School of Information Science and Technology,Yunnan Normal University;  
In order to solve the bottleneck problems of processing speed and resource allocation of Alternating Least Squares( ALS),a distributed parallel matrix factorization recommendation approach with similar user index was proposed. First,the approach found nearest neighbors among the users based on their ratings; Then,Spark was employed to implement the proposed approach,and the recommendation to the user is produced. Simulate experiments in Movie Lens datasets provided by Group Lens website show that the proposed algorithm can resolve the issue of low execution efficiency of ALS for large-scale datasets and the worse scalability in clouds.
【Fund】: 云南省教育厅科学研究基金资助项目(2014Y145)
【CateGory Index】: TP391.3
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