A fast and accurate collaborative filter

Author

Deng, Wanyu ; Zheng, Qinghua ; Chen, Lin

Author_Institution

Dept. of Comput. Sci. & Technol., Xi´´an Jiao tong Univ., Xi´´an, China

fYear

2009

fDate

17-19 Aug. 2009

Firstpage

132

Lastpage

135

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.

Keywords

information filtering; iterative methods; learning (artificial intelligence); singular value decomposition; collaborative filtering; extreme learning machine; iterative algorithms; long-consuming training; reduced-SVD technique; Collaboration; Collaborative work; Computer science; Filtering algorithms; Information filtering; Information filters; Iterative algorithms; Machine learning; Predictive models; Supervised learning; Extreme Learning Machine; collaborative filtering; recommendation;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing, 2009, GRC '09. IEEE International Conference on

Conference_Location

Nanchang

Print_ISBN

978-1-4244-4830-2

Type

conf

DOI

10.1109/GRC.2009.5255149

Filename

5255149

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3424484