The recommender system can be viewed as a matrix completion problem, which aims to predict unknown values within a matrix. Solutions to this problem are categorized into two approaches: transductive and inductive reasoning. In transductive reasoning, the model cannot be applied to new cases unseen during training. In contrast, IGMC, the state-of-the-art inductive algorithm, only requires subgraphs for target users and items, without needing any other content information. While the absence of a requirement for content information simplifies the model and enhances transferability to new tasks, incorporating content information could still improve the model's performance. In this article, the authors introduce Hi-GMC, a hybrid version of the IGMC model that incorporates content information alongside users and items. They present a novel graph model to encapsulate the side information related to users and items and develop a learning method based on graph neural networks. This proposed method achieves state-of-the-art performance on the MovieLens-100K dataset for both warm and cold start scenarios.