Title :
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
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;
Conference_Titel :
Granular Computing, 2009, GRC '09. IEEE International Conference on
Conference_Location :
Nanchang
Print_ISBN :
978-1-4244-4830-2
DOI :
10.1109/GRC.2009.5255149
