Recommendation systems are considered as one of the important components of e-commerce platforms due to their direct impact on profitability. In this study, we propose a hierarchical recommendation system to increase the performance of the e-commerce recommendation system. Our DeepIDRS approach has a two-level hierarchical structure: (1) The first level uses bidirectional encoder representations to represent textual information of an item (title, description, and a subset of item reviews), efficiently and accurately; (2) The second level is an attention-based sequential recommendation model that uses item embeddings derived from the first level of the hierarchical structure. Furthermore, we compare our approach DeepIDRS with various approaches from different perspectives. Our results in the real-world dataset show that DeepIDRS provides at least 10% better HR@10 and NCCG@10 performance than other review-based models. With this study, for e-commerce, we clearly show that a hierarchical, explainable recommendation system that accurately represents the item title, description, and a subset of item reviews, improves performance.