In a variety of recommender systems, items, such as news or articles, are associated with text. Most of previous recommender systems learn item embeddings from the textual content by utilizing the bag-of-words technique. However, due to its limited ability to capture semantic meanings in the text, these methods lead to the shallow modeling of items. Recently proposed deep learning based methods try to overcome the limitation by leveraging Recurrent Neural Networks (RNN). Suffering from the problem of modeling long sequences for RNN, these methods are unable to effectively model items based on their textual content as well. In this paper, in order to overcome aforementioned limitations and accurately capture semantic meanings within the textual content, we propose Hierarchical Collaborative Embedding (HCE). HCE tightly couples a Hierarchical Recurrent Network (HRN) with Probabilistic Matrix Factorization (PMF) to provide top-N ranking lists of items for users. In the experiments, we show that HCE beats strong baselines by a wide margin on three real-world datasets.