Fusing deep neural networks and aspect-aware for explainable recommendation method
To improve the accuracy and interpretability of recommendations,a method of interpretability recommendation fusing deep neural network and aspect-aware is proposed.First,for the sparsity problem of rating data,the explicit and im-plicit rating data are comprehensively considered,and the latent features of users and items are learned through the matrix decomposition of deep neural networks.Second,the aspect features of users and items are learned through the unsupervised aspect extraction module.Then,the latent features and aspect features are integrated into the prediction layer for rating pre-diction.Finally,for the problems of low explanation quality and lack of personalization,based on the rating prediction,the extracted topic and predefined neural templates are combined to generate recommendation reason to improve the generation quality of explanations.Experimental results on Amazon data sets show that the proposed model can not only accurately pre-dict user ratings of items,but also generate explanatory reasons for recommendations.In comparison,the explanation quali-ty generated by this method is somewhat better than that of the contrastive methods.
万方数据
维普
