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| An Improved Singular Value Decomposition Recommender Algorithm Based on Local Structures |
| Fang Yao-ning① Guo Yun-fei① Ding Xue-tao② Lan Ju-long① |
①(National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450002, China)
②(School of Software, Tsinghua University, Beijing 100084, China) |
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Abstract Recommender algorithms based on Singular Value Decomposition (SVD) performs better in prediction accuracy and stability, while the speed of error descent slows down gradually and the iteration number is huge when applying Stochastic Gradient Descent (SGD) to SVD , which greatly limits its usage in actual projects. To address this problem, the difference matrix of the rating matrix is used to represent the local structures and to optimize SVD as a new target function. Experiments on MovieLens and Netflix dataset show that the improved SVD algorithm can obtain better performances with fewer iterations compared to classic SVD. When compared with other state-of-the-art recommender algorithms, the proposed algorithm is only second to SVD++, however within far less training time than SVD++.
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Received: 12 October 2012
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Corresponding Authors:
Fang Yao-ning
E-mail: fyn07@163.com
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2013, Vol. 35 
