## 5 Experiments
### Ablation Experiments (RQ2)  
To investigate the contribution of each key component in our proposed GLARA model, we conduct an ablation study. In addition to comparing the performance of the full model with the ablated versions, statistical significance tests (such as \(t\)-tests) were performed to ensure that the observed differences were statistically significant. The performance differences between the full model and the ablated versions were consistently significant, with \(p\)-values of less than 0.05. Moreover, the standard deviation across 10 runs was calculated to assess the stability of the results. * GLARA w/o VRKG: Disable virtual relationship clustering and keep the original relationship type. * GLARA w/o LWS: Disable LWS smoothing and use the raw knowledge graph directly. * GLARA w/o GAT: Replacing GAT attention with mean pooling. The results in Table 3 show that the full model performs significantly better than all variants:  
The ablation results clearly indicate that each component plays a critical role in the overall effectiveness of the model. Specifically, removing any one of VRKG, LWS, or GAT results in a noticeable drop across all metrics on both datasets, though to varying degrees. The reasons for these degradations are analyzed below:  
1. Removal of VRKG (w/o VRKG): When we eliminate the Virtual Relational Knowledge Graph, the model relies solely on the raw relations from the original knowledge graph. In this setting, long-tail relations are treated as independent and isolated, lacking semantic clustering or abstraction. As a result, the model suffers from relation sparsity, leading to poor generalization for infrequent entity pairs. This is especially detrimental for low-frequency or cold-start items, where VRKG's semantic grouping plays a vital role in enhancing connectivity and embedding robustness. 2. Removal of LWS (w/o LWS): Without Local Weighted Smoothing, the model loses its semantic denoising and neighbor regularization mechanism. The original LWS module helps smooth item embeddings by incorporating information from semantically similar neighbors within each virtual relation group. Removing it disrupts the global semantic consistency in the representation space and makes the model more sensitive to noise from sparse \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Recall@20** & **Recall@20 Std Dev** & \(p\)**-Value (Recall)** & **NDCG@20** & **NDCG@20** & \(p\)**-Value (NDCG)** \\ \hline w/o VRKG & 0.3379 & 0.013 & 0.03 & 0.3162 & 0.012 & 0.04 \\ w/o LWS & 0.3251 & 0.015 & 0.02 & 0.3085 & 0.013 & 0.03 \\ w/o GAT & 0.3432 & 0.017 & 0.045 & 0.3216 & 0.014 & 0.05 \\ Full Model & 0.3627 & 0.013 & - & 0.3325 & 0.011 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different GLARA versions. or noisy connections. In particular, the model becomes overly dependent on the raw structure of the graph, which can be unstable and noisy, especially in datasets with long-tail distributions. 3. Removal of the GAT (w/o the GAT): Disabling the Graph Attention Network results in the most significant performance drop. This is because the GAT serves as the core mechanism for modeling local dynamic user behavior. Without it, the model can no longer assign adaptive importance to recent or behaviorally relevant interactions, nor can it effectively capture temporal preference shifts. Additionally, the lack of attention leads to uniform weighting of neighbors, which is both inefficient and susceptible to irrelevant or noisy signals. This highlights that the GAT is essential for capturing personalized, time-sensitive interaction patterns, and complements the global smoothing of LWS by focusing on high-resolution local signals. Taken together, these results confirm that VRKG, LWS, and the GAT each contribute unique and complementary strengths to the model.