This document discusses a new approach called HERec for recommendations using heterogeneous information network (HIN) embedding. HERec first uses meta-path based random walks to generate node sequences for network embedding. The learned embeddings are transformed and integrated into an extended matrix factorization model for rating prediction. Experiments on three datasets show HERec effectively improves recommendation performance and addresses cold-start issues by exploiting embedded HIN information.