A fast and accurate collaborative filter

W. Deng, Qinghua Zheng, Lin Chen
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引用次数: 2

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.
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快速准确的协同过滤
协同过滤存在两个关键问题:维度诅咒和训练耗时长。在本文提出的算法中,提出的svd技术有效地解决了维数诅咒问题,并采用极限学习机(ELM)解决了耗时长的训练问题,其速度比迭代算法(如BP)快数百倍。这将使算法更加准确和快速。
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