ProGraph: Graph Prompt Tuning with Knowledge-aware Contrastive Learning for Recommendation

Graph Neural Networks (GNNs) have demonstrated strong representation learning capabilities in recommender systems, particularly under the contrastive learning paradigm, where the construction of positive and negative sample pairs effectively captures latent relations between users and items, thereby significantly enhancing recommendation performance. However, existing graph contrastive learning methods predominantly rely on static augmentation strategies, lacking adaptability to diverse user behaviors and semantic structures. Moreover, effectively integrating external knowledge (e.g., user attributes and item semantics) into the contrastive learning process remains a major challenge. To address these limitations, we propose ProGraph, a graph prompt tuning framework tailored for recommendation tasks. ProGraph introduces adaptive contrastive learning within the graph prompt mechanism, enhanced by knowledge-aware guidance, to improve both the discriminability and semantic generalization of learned representations. Specifically, it employs structured prompts to guide GNNs in learning embeddings across multiple semantic subspaces, while incorporating knowledge-assisted graph views to preserve structural consistency and better handle heterogeneous attributes. Unlike traditional full-parameter optimization, ProGraph enables efficient tuning with a small number of learnable prompt parameters, thus achieving better transferability and modular compatibility. Extensive experiments on three real-world recommendation datasets with rich interaction records and knowledge attributes demonstrate that ProGraph consistently outperforms several representative state-of-the-art baselines in top-K recommendation performance.