DAME: Depth-Aware Hybrid Structural Mixture-of-Experts for Graph Recommendation

Graph neural networks have achieved remarkable success in collaborative recommendation tasks by modeling high-order connections. However, existing methods often force diverse behaviors to follow homogenized aggregation paths, fail to disentangle complex user intents, and lack explicit modeling of the hierarchical semantics inherent in propagation depth. To address this, we propose DAME, a Depth-Aware Mixture-of-Experts framework for graph-based recommendation. DAME employs a structural expert mechanism to achieve node-adaptive graph propagation and incorporates Rotational Embedding Propagation, integrating propagation depth information into node representations through layer-wise orthogonal rotations. This design combines adaptive modeling of interaction structures with explicit depth encoding, facilitating the distinction between shallow and high-order collaborative signals. Experiments on three public datasets demonstrate that DAME achieves state-of-the-art performance. Ablation studies further validate the effectiveness of these two components, while routing analysis reveals the correlation between expert specialization and user interaction patterns.