基于先验知识的非负矩阵半可解释三因子分解算法

Partially explainable non-negative matrix tri-factorization algorithm based on prior knowledge

非负矩阵三因子分解是潜在因子模型中的重要组成部分,由于能将原始数据矩阵分解为三个相互约束的潜因子矩阵,被广泛应用于推荐系统、迁移学习等研究领域,但目前还没有非负矩阵三因子分解的可解释性方面的研究工作。鉴于此,将用户评论文本信息当作先验知识,设计了一种基于先验知识的非负矩阵半可解释三因子分解(PE-NMTF)算法。首先利用情感分析技术提取用户评论文本信息的情感极性偏好;然后更改了非负矩阵三因子分解算法的目标函数和更新公式,巧妙地将先验知识嵌入到算法中;最后在推荐系统冷启动任务的Yelp和Amazon数据集以及图像零次识别任务的AwA和CUB数据集上与非负矩阵分解、非负矩阵三因子分解算法做了大量对比实验,实验结果表明所提算法在均方根误差(RMSE)、归一化折损累计增益(NDCG)、归一化互信息(NMI)和准确率(ACC)上都表现优异,且利用先验知识进行非负矩阵三因子分解的解释具有可行性和有效性。

Non-negative Matrix Tri-Factorization(NMTF)is an important part of the latent factor model.Because this algorithm decomposes the original data matrix into three mutually constrained latent factor matrices,it has been widely used in research fields such as recommender systems and transfer learning.However,there is no research work on the interpretability of non-negative matrix tri-factorization.From this view,by regarding the user comment text information as prior knowledge,Partially Explainable Non-negative Matrix Tri-Factorization(PE-NMTF)algorithm was designed based on prior knowledge.Firstly,sentiment analysis technology was used by to extract the emotional polarity preferences of user comment text information.Then,the objective function and updating formula in non-negative matrix tri-factorization algorithm were changed,embedding prior knowledge into the algorithm.Finally,a large number of experiments were carried out on the Yelp and Amazon datasets for the cold start task of the recommender system and the AwA and CUB datasets for the image zero-shot task to compare the proposed algorithm with the non-negative matrix factorization and the non-negative matrix three-factor decomposition algorithms.The experimental results show that the proposed algorithm performs well on RMSE(Root Mean Square Error),NDCG(Normalized Discounted Cumulative Gain),NMI(Normalized Mutual Information),and ACC(ACCuracy),and the feasibility and effectiveness of the non-negative matrix tri-factorization were verified by using prior knowledge.