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| A Bayesian Probabilistic Matrix Factorization Algorithm Based on Logistic Function |
| Fang Yao-ning Guo Yun-fei Lan Ju-long |
| National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450002, China |
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Abstract The matrix factorization is one of the most powerful tools in collaborative filtering recommender systems. The Bayesian Probabilistic Matrix Factorization (BPMF) model has advantages of high prediction accuracy, but can not capture non-linear relationships between latent factors. To address this problem, an improved model is proposed based on the Logistic function and Markov Chain Monte Carlo is used to train the proposed model. Experiments on two real-world benchmark datasets show significant improvements in prediction accuracy compared with several state-of-the-art methods for recommendation tasks.
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Received: 19 April 2013
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Corresponding Authors:
Fang Yao-ning
E-mail: fyn07@163.com
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2014, Vol. 36 
