基于Logistic函数的贝叶斯概率矩阵分解算法

在协同过滤推荐系统中，矩阵分解是一种非常有效的工具。贝叶斯概率矩阵分解模型具有预测精度高的优点，但不能表示潜在因子之间的非线性关系。针对该问题，该文提出一种基于Logistic函数的改进贝叶斯概率矩阵分解模型，并使用马尔科夫链蒙特卡罗方法进行训练。在两组真实数据集合上的实验表明，基于Logistic函数的贝叶斯概率矩阵分解算法能够明显提高预测准确性，有效缓解数据稀疏性问题。

A Bayesian Probabilistic Matrix Factorization Algorithm Based on Logistic Function

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.