The users’ historical interactions usually contain their interests and purchase habits based on which personalised recommendations can be made. However, such user interactions are often sparse, leading to the well-known cold-start problem when a user has no or very few interactions. In this paper, we propose a new recommendation model, named Heterogeneous Graph Neural Recommender (HGNR), to tackle the cold-start problem while ensuring effective recommendations for all users. Our HGNR model learns users and items’ embeddings by using the Graph Convolutional Network based on a heterogeneous graph, which is constructed from user-item interactions, social links and semantic links predicted from the social network and textual reviews. Our extensive empirical experiments on three public datasets demonstrate that HGNR significantly outperforms competitive baselines in terms of the Normalised Discounted Cumulative Gain and Hit Ratio measures.