We present an Information Retrieval framework that leverages Heterogeneous Information Network (HIN) embeddings for contextual suggestion. Our method represents users, documents and other context-related documents as heterogeneous objects in a HIN. Using meta-paths, selected based on domain knowledge, we create graph embeddings from this network, thereby learning a representation of users and objects in the same semantic vector space. This allows inferences of user interest on unseen objects based on distance in the embedding space. These object distances are then incorporated as features in a well-established learning to rank (LTR) framework. We make use of the 2016 TREC Contextual Suggestion (TRECCS) dataset, which contains user profiles in the form of relevance-rated documents, and demonstrate the competitiveness of our approach by comparing our system to the best performing systems of the TRECCS task.