山东大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (3): 65-73.doi: 10.6040/j.issn.1672-3961.2.2015.080
庞俊涛1, 张晖2*, 杨春明1, 李波1,3, 赵旭剑1
PANG Juntao1, ZHANG Hui2*, YANG Chunming1, LI Bo1,3, ZHAO Xujian1
摘要: 为解决已有关于多指标评分推荐方法中忽略多指标之间存在相关性的问题,提出一种基于概率矩阵分解的多指标协同过滤算法(multi-criteria collaborative filtering algorithm based on probabilistic matrix factorization, MCPMF)。该算法将多指标评分表示成一个对整体用户和产品产生影响的权重矩阵,并假设该矩阵潜在分布服从高斯分布,其概率密度分布与用户和产品特征矩阵的概率密度分布条件相关。通过概率矩阵分解的方法学习得到用户和产品特征矩阵。在两个真实数据集上的试验结果表明,该方法比只考虑单一综合评分的方法能更加精确地预测用户的综合评分,同时能降低数据稀疏对推荐算法的影响。
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