计算机科学 ›› 2017, Vol. 44 ›› Issue (5): 285-289.doi: 10.11896/j.issn.1002-137X.2017.05.052
刘付勇,高贤强,张著
LIU Fu-yong, GAO Xian-qiang and ZHANG Zhu
摘要: 针对现有基于矩阵分解的协同过滤推荐系统预测精度与推荐精度较低的问题,提出一种改进的矩阵分解方法与协同过滤推荐系统。首先,将评分矩阵分解为两个非负矩阵,并对评分做归一化处理,使其具有概率语义;然后,采用变分推理法计算贝叶斯概率模型实部后验的分布;最后,搜索相同偏好的用户分组并预测用户的偏好。此外,基于用户向量的稀疏性设计一种低计算复杂度、低存储成本的推荐结果决策算法。基于3组公开数据集的实验结果表明,本算法的预测性能以及推荐系统的效果均优于其他预测算法与推荐算法。
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