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A personalized recommendation algorithm based on approximating the singular value decomposition (ApproSVD)

Zhou, Xun, He, Jing, Huang, Guangyan and Zhang, Yanchun 2012, A personalized recommendation algorithm based on approximating the singular value decomposition (ApproSVD), in WI-IAT 2012: Proceedings of the 11th IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IEEE, Piscataway, N.J., pp. 458-464, doi: 10.1109/WI-IAT.2012.225.

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Title A personalized recommendation algorithm based on approximating the singular value decomposition (ApproSVD)
Author(s) Zhou, Xun
He, Jing
Huang, Guangyan
Zhang, Yanchun
Conference name International Conferences on Web Intelligence and Intelligent Agent Technology (11th: 2012: Macau, China)
Conference location Macau, China
Conference dates 4-7 Dec. 2012
Title of proceedings WI-IAT 2012: Proceedings of the 11th IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Publication date 2012
Start page 458
End page 464
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Personalized recommendation is, according to the user's interest characteristics and purchasing behavior, to recommend information and goods to users in which they may be interested. With the rapid development of Internet technology, we have entered the era of information explosion, where huge amounts of information are presented at the same time. On one hand, it is difficult for the user to discover information in which he is most interested, on the other hand, general users experience difficult in obtaining information which very few people browse. In order to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm based on approximating the singular value decomposition (SVD) in this paper. 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. 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 the Movie Lens dataset, and show that our method has the best prediction quality. © 2012 IEEE.
ISBN 9780769548807
Language eng
DOI 10.1109/WI-IAT.2012.225
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2012, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083691

Document type: Conference Paper
Collection: School of Information Technology
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