基于潜在因子多样性的非负矩阵分解协同过滤模型

基于非负矩阵分解的协同过滤模型在高维稀疏数据的预测和填补上十分有效，该模型具有推荐个性化、有效利用其他相似用户回馈信息的优点，但也存在预测精度较低等不足。针对用户或项目在不同情景下的评分差异性，提出了一种改进的基于潜在因子多样性的非负矩阵分解的协同过滤模型。该模型充分考虑在不同情境下，用户和项目潜在特征矩阵的多样性，在模型的训练中，采用了单元素非负乘法更新规则和交替方向法，保证了目标矩阵的非负性，且提高了模型的收敛率。在真实的工业数据集上的实验结果表明，相比于经典的非负矩阵分解模型，该模型的预测精度有了明显提高。

A collaborative filtering model of non-negative matrix factorization based on diversity of latent factors

A collaborative filtering model of non-negative matrix factorization based on diversity of latent factors is effective in predicting high dimension and sparse matrix. This model can recommend personally and utilize the feedback information from other similar users effectively. However, it has the disadvantage of low prediction accuracy. Due to the diversity of ratings for users or items under different circumstances, a collaborative filtering model based on non-negative matrix factorization was proposed. The model considered the diversity of latent characteristic matrices for users and items. The single-element non-negative multiplication update rule and the principle of alternate direction method were integrated in the training of the model, which not only guaranteed the non-negativity of the target matrix, but also improved the convergence rate of the models. Finally, experiments were carried out on real industrial data sets. The experimental results show that the prediction accuracy of the proposed model is higher than that of the classical non-negative matrix factorization model.