SVD系列算法在评分预测中的过拟合现象

主要对协同过滤推荐算法进行改进,以使训练评分模型的过程能够预防过拟合现象的发生。对SVD系列算法在评分预测问题中产生的过拟合现象进行相关实验与研究,提出通过调整算法参数与迭代次数来避免过拟合现象发生的方法。实验结果表明,该方法能够以较高的时间效率找到评分预测结果较好的结果,并可有效地避免过拟合现象的发生。

Overfitting phenomenon of the series of single value decompositionalgorithms in rating prediction

The collaborative filtering algorithm was improved to prevent overfitting in training rating prediction model. Experiments were put forth for overfitting phenomenon of the series of single value decomposition algorithms in rating prediction, and a method of adjusting parameters and iteration count to avoid overfitting phenomenon was proposed. The experimental results showed that this method could find better rating prediction and avoid overfitting at the same time.