Graph neural network-based recommender system models have gained popularity in the recent past due to their effective representation of user-item interactions in the latent feature space. Among them, item sequence-based collaborative filtering models are widely explored, which use the sequence of items consumed by different users and try to generate a set of items that may suit a new user. However, this item sequence generation only relies on other users’ past behaviors across different layers in a GNN framework and does not provide any intuitive reasoning behind the recommendation generation. Furthermore, the item-embedding information propagated across different layers may not provide sufficient user preference information towards items. To alleviate this, we propose a model, i.e., FIGNNCF, that uses the sequence-based recommendation technique but with a feature-based approach. The item features are integrated into the embeddings to propagate user preference information. Additionally, our proposed approach only uses a user-item bipartite graph and eliminates the item-item sequences graph, reducing the time required for training while maintaining the recommendation accuracy. The feature information is propagated using a one-hot encoding vector, which defines the model’s simplicity. The proposed model significantly improves performance when tested on three benchmark datasets using standard evaluation measures.