Prompt-Guided Graph Contrastive Learning with Controllable Augmentation and Fine-Grained Preference Modeling for Recommendation

Graph contrastive learning (GCL) is a promising approach for improving collaborative filtering (CF), especially in sparse interaction settings. Despite their effectiveness, most existing GCL-based recommendation methods risk disrupting meaningful collaborative signals by relying on uncontrollable graph augmentations. Besides, these methods fail to explicitly capture and integrate diverse fine-grained preferences because they model user-item interactions at a coarse level. To address these issues, we propose P rompt-guided G raph C ontrastive L earning (PGCL), a novel framework that introduces learnable prompt nodes to construct controllable augmented graphs and enable fine-grained preference modeling. Specifically, these prompt nodes serve as latent fine-grained preferences and can be dynamically connected to user and item nodes in the graph based on semantic relevance. With this prompt-guided augmented graph, PGCL then incorporates two modules: the prompt-guided and the interaction-enhanced contrastive learning. The former adaptively aligns users and items with their most semantically relevant prompt nodes, enabling the model to capture and distinguish multi-dimensional preference signals. The latter incorporates real user-item interaction pairs as supervision, mitigating potential bias from prompt semantics and reinforcing collaborative signal learning. Together, these designs enable PGCL to model fine-grained preferences via a more semantically guided augmentation process. Extensive experiments across five public datasets demonstrate that PGCL consistently outperforms state-of-the-art baselines, thereby validating its effectiveness for recommendation tasks.