NDLI: A fast and accurate collaborative filter

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A fast and accurate collaborative filter

Content Provider	IEEE Xplore Digital Library
Author	Wanyu Deng Qinghua Zheng Lin Chen
Copyright Year	2009
Description	Author affiliation: MOE KLINNS Lab and SKLMS Lab, Department of Computer Science and Technology, Xi'an Jiao tong University, China (Wanyu Deng; Qinghua Zheng) \|\| Xi'an Institute of Post & Telecommunications, China (Lin Chen)
Abstract	There are two key issues for collaborative filtering: curse of dimension and long-consuming training. In our proposed algorithm, the curse of dimension problem is resolved by the proposed Reduced-SVD technique effectively and long-consuming training is addressed by Extreme Learning Machine (ELM) which is hundreds of times faster than iterative algorithms (e.g. BP). This will enable the algorithm more accurate and faster.
Starting Page	132
Ending Page	135
File Size	255087
Page Count	4
File Format	PDF
ISBN	9781424448302
DOI	10.1109/GRC.2009.5255149
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	collaborative filtering Extreme Learning Machine Predictive models recommendation Information filtering Computer science Supervised learning Collaboration Machine learning Filtering algorithms Collaborative work Information filters Iterative algorithms
Content Type	Text
Resource Type	Article

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