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A personalized recommendation algorithm based on approximating the singular value decomposition (ApproSVD)
conference contribution
posted on 2012-12-01, 00:00 authored by X Zhou, J He, Guangyan HuangGuangyan Huang, Y ZhangPersonalized 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.
History
Event
IEEE/WIC/ACM International Conference on Intelligent Agent Technology (11th : 2012 : Macau, China)Volume
2Pagination
458 - 464Publisher
IEEELocation
Macau, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2012-12-04End date
2012-12-07ISBN-13
9780769548807Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2012, IEEETitle of proceedings
WI-IAT 2012: Proceedings of the 11th IEEE/WIC/ACM International Conference on Intelligent Agent TechnologyUsage metrics
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