Because deep learning models have been used successfully in various fields during recent years, many recommendation systems have been developed using deep learning techniques. However, although deep learning–based recommendation systems have achieved high recommendation performance, their lack of interpretability may reduce users’ trust and satisfaction. In this study, we aimed to predict and recommend the purchase of funds by customers in the next month while simultaneously providing relevant explanations. To achieve this goal, we employed a knowledge graph structure and deep learning techniques to embed features of customers and funds into a unified latent space. With the proposed structure, we learned some information that could not be learned using traditional deep learning models and obtained personalized recommendations and explanations simultaneously. Moreover, we obtained complex explanations by changing the training procedure of the model and developed a measure for rating the customized explanations according to their strength and uniqueness. Finally, we obtained some possible special recommendations based on the knowledge graph structure. By evaluating the data set of mutual fund transaction records, we verified the effectiveness of the developed model for providing precise recommendations. We also conducted some case studies of explanations to demonstrate the effectiveness of the developed model for providing usual explanations, complex explanations, and other special recommendations.