## 5 Experiments
### Parameter Sensitivity Analysis (RQ3)  
To evaluate the sensitivity of our model to key hyperparameters, we analyze the impact of varying the number of LWS iterations Q and GAT layers L on overall recommendation performance. This analysis provides further insight into how the balance between global semantic smoothing (via LWS) and local attention-based adaptation (via GAT) affects the model's ability to adapt to evolving user preferences and effectively handle long-tail data distributions. We conduct experiments on the Last.FM and MovieLens-1M datasets with the number of virtual relations K set to 3. Figure 2 shows the performance on both datasets for Recall@20 and NDCG@20.  
As shown in Figure 2a,b, we fix the number of GAT layers L and vary the number of iterations Q in the range 1, 2, 3, 4. We notice that the performance curve also increases and then decreases on the Last.FM dataset. The results show that as Q increases, items will be closer to similar neighbors in the embedding space will benefit from the item representation in the recommendation task. However, as Q increases, the node embeddings will be too close to differentiate, thus impairing the model performance. In addition, the performance tends to decrease when iterating over MovieLens-1M with increasing Q, because the knowledge graph of MovieLens-1M is richer and has fewer entities than Last.FM, and the dense connectivity makes the embeddings more prone to over-smoothing.  
In the context of this study, the number of iterations, Q, was fixed at a particular value, and the value of L was varied within a specific range. The results demonstrated an initial enhancement in performance, followed by a subsequent decline. Specifically, as L increases from 1 to 2, the attention mechanism is able to capture higher-order semantic information (e.g., key connections in long-tail relationships) more accurately by dynamically assigning the neighbor weights, thus significantly improving the recommendation effect. However, when L = 3, although the attention mechanism mitigates the oversmoothing problem of uniform aggregation, the attention weights of some paths in deep propagation may fail due to semantic dilution or noise interference, resulting in a slight performance degradation. This phenomenon is more pronounced in sparsely-connected KGs (e.g., Last.FM), where the complexity of the deep propagation paths exacerbates the difficulty of attention allocation, and in densely-connected KGs (e.g., MovieLens-1M), where attentional redundancy may further erode the gains of the deep structure.  
In summary, as the layer L increases, the model performance initially improves significantly and subsequently declines uniformly, independent of the number of iterations Q. This indicates that the GAT layer L is the primary determinate of model performance, and its attentional mechanism enhances the directionality of information propagation, yet does not fully address the inherent limitations of deep propagation. The number of layers L determines the depth of the receptive field, and stacking L layers enables the item embedding to fuse multi-hop neighbor information. The number of iterations Q controls the similarity between the embedding and its local neighborhood by adjusting the smoothing degree of the first-order neighbors. The attentional mechanism further enhances the weight assignment of critical neighbors; however, peak performance is still limited by the reasonable choice of the number of layers L.