Recently, online shopping has become very prominent. Furthermore, most e-commerce platforms use recommender systems to suggest products to the user for potential purchase to help enhance the user experience. Existing recommender models determine the correlation between user profiles and product information to make product recommendations. Although current recommender systems can accurately suggest products, there is a lack of transparency in terms of how the recommendations are made. As a result, users may not trust the suggestions made by what could be perceived as a black box. To address this problem, we have designed a deep recommender system that discovers user preferences and provides the reasons behind the recommendations it makes by examining clues such as user reviews and item descriptions. We adopt the attention mechanism to enhance text analysis capability, which not only enables accurate recommendations but also shows the relationship between products and user profiles. Furthermore, we conduct a case study in which we utilize feature weights to present the reasons to the user by observing attention maps. The results of our experiment show that our model can consistently provide accurate recommendations and justifications to the user. Moreover, our model can handle new items and users, that is, data that is not part of the initial training dataset. Our recommender system is a novel solution that leverages the advantages of content-based and collaborative filtering.