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Scalable approximating SVD algorithm for recommender systems

Zhou, Xun, He, Jing, Huang, Guangyan and Zhang, Yanchun 2014, Scalable approximating SVD algorithm for recommender systems, Web intelligence and agent systems, vol. 12, no. 4, pp. 359-373, doi: 10.3233/WIA-140303.

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Title Scalable approximating SVD algorithm for recommender systems
Author(s) Zhou, Xun
He, Jing
Huang, Guangyan
Zhang, Yanchun
Journal name Web intelligence and agent systems
Volume number 12
Issue number 4
Start page 359
End page 373
Publisher IOS Press
Place of publication Amsterdam, Netherlands
Publication date 2014
ISSN 1570-1263
1875-9289
Keyword(s) experimental evaluation
incremental algorithm
recommendation system
Singular value decomposition
Summary With the rapid development of Internet, the amount of information on the Web grows explosively, people often feel puzzled and helpless in finding and getting the information they really need. For overcoming this problem, recommender systems such as singular value decomposition (SVD) method help users finding relevant information, products or services by providing personalized recommendations based on their profiles. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data. Thus, to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm which is called ApproSVD algorithm based on approximating SVD in this paper. The trick behind our algorithm is to sample some rows of a user-item matrix, rescale each row by an appropriate factor to form a relatively smaller matrix, and then reduce the dimensionality of the smaller matrix. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on MovieLens dataset and Flixster dataset, and show that our method has the best prediction quality. Furthermore, in order to show the superiority of the ApproSVD algorithm, we also conduct an empirical study to compare the prediction accuracy and running time between ApproSVD algorithm and incremental SVD algorithm on MovieLens dataset and Flixster dataset, and demonstrate that our proposed method has better performance overall.
Language eng
DOI 10.3233/WIA-140303
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 810105 Intelligence
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, IOS Publishing
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070659

Document type: Journal Article
Collection: School of Information Technology
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