Recommendation systems play a crucial role in uncovering concealed interactions among users and items within online social networks. Recently, Graph Neural Network (GNN)-based recommendation systems exploit higher-order interactions within the user-item interaction graph, demonstrating cutting-edge performance in recommendation tasks. However, GNN-based recommendation models are susceptible to different types of noise attacks, such as deliberate perturbations or false clicks. These attacks propagate through the graph and adversely affect the robustness of recommendation results. Conventional two-stage method that purifies the graph before training the GNN model is suboptimal. To strengthen the model’s resilience to noise, we propose Graph Structure Learning for Robust Recommendation (GSLRRec), a joint learning framework that integrates graph structure learning and GNN model training for recommendation. Specifically, GSLRRec considers the graph adjacency matrix as adjustable parameters, and simultaneously optimizes both the graph structure and the representations of user/item nodes for recommendation. During the joint training process, the graph structure learning employs low-rank and sparse constraints to effectively denoise the graph. Our experiments illustrate that the simultaneous learning of both structure and GNN parameters can provide more robust recommendation results under various noise levels.