计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 540-545.doi: 10.11896/JsJkx.191000172
马海江
MA Hai-Jiang
摘要: 用户评分数据的稀疏性和上下文的信息缺失,往往导致基于矩阵分解(Matrix Factorization,MF)的推荐算法在准确性方面有所欠缺。针对此问题,文中提出了一种基于卷积神经网络(Convolutional Neural Networks,CNN)与约束概率矩阵分解(Constrained Probabilistic Matrix Factorization,CPMF)的推荐算法。首先,构建卷积神经网络模型,对用户上下文辅助信息进行识别,获得文本潜在向量,并叠加高斯噪声,初始化项目特征矩阵;然后,根据用户评分信息,利用约束矩阵来约束用户特征,并叠加补偿矩阵,初始化用户特征矩阵;接着,利用初始化的用户特征矩阵和项目特征矩阵拟合评分矩阵,对评分矩阵进行矩阵分解,并利用坐标下降算法更新参数;最后,预测用户对项目的评分,实现项目推荐。在Movielens和Amazon数据集上的实验结果表明,该推荐算法显著优于传统的推荐模型,有效地提高了推荐结果的准确率。
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