南京大学学报(自然科学版) ›› 2022, Vol. 58 ›› Issue (1): 135–142.doi: 10.13232/j.cnki.jnju.2022.01.013
• • 上一篇
一种可解释性泛化矩阵分解推荐算法
吕亚兰, 徐媛媛, 张恒汝(
)
- 西南石油大学计算机科学学院，成都，610500
An explainable generalized matrix factorization recommendation algorithm
Lü Yalan, Yuanyuan Xu, Hengru Zhang()
- School of Computer Science，Southwest Petroleum University，Chengdu，610500，China
摘要：
可解释性矩阵分解解决了概率矩阵分解缺乏可解释性的问题，然而概率矩阵分解假设评分数据是服从正态分布的，这与实际场景有一定的偏差.针对该问题，提出一种可解释性泛化矩阵分解推荐算法.首先采用一种新型的变换函数使原始评分近似服从正态分布，然后通过可解释性矩阵分解获得预测评分，最后利用对应的逆变换函数将预测评分映射回原始评分区间.在三个数据集上进行实验，结果表明，与多个主流矩阵分解算法相比，提出的算法在多个评价指标上占优.
中图分类号:
- TP181
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