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SVD-based incremental approaches for recommender systems
journal contribution
posted on 2015-06-01, 00:00 authored by X Zhou, J He, Guangyan HuangGuangyan Huang, Y ZhangDue to the serious information overload problem on the Internet, recommender systems have emerged as an important tool for recommending more useful information to users by providing personalized services for individual users. However, in the “big data“ era, recommender systems face significant challenges, such as how to process massive data efficiently and accurately. In this paper we propose an incremental algorithm based on singular value decomposition (SVD) with good scalability, which combines the Incremental SVD algorithm with the Approximating the Singular Value Decomposition (ApproSVD) algorithm, called the Incremental ApproSVD. Furthermore, strict error analysis demonstrates the effectiveness of the performance of our Incremental ApproSVD algorithm. We then present an empirical study to compare the prediction accuracy and running time between our Incremental ApproSVD algorithm and the Incremental SVD algorithm on the MovieLens dataset and Flixster dataset. The experimental results demonstrate that our proposed method outperforms its counterparts.
History
Journal
Journal of computer and system sciencesVolume
81Issue
4Pagination
717 - 733Publisher
Academic PressLocation
Maryland Heights, MOPublisher DOI
ISSN
0022-0000eISSN
1090-2724Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2015, Academic PressUsage metrics
Categories
Keywords
Experimental evaluationIncremental algorithmRecommender systemSingular value decompositionScience & TechnologyTechnologyComputer Science, Hardware & ArchitectureComputer Science, Theory & MethodsComputer ScienceALGORITHMMATRIXInformation SystemsComputation Theory and MathematicsDistributed Computing
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