# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
## 4 Experiments
### Experimental Results and Analysis  
We evaluated the above seven algorithms using the NDCG, F1-Score, and Recall metrics, assessing the recommendation capabilities of the top 5, top 10, and top 15 recommendations for each algorithm. To ensure fair comparative experiments, each algorithm used the parameter settings from the original articles and employed the Bayesian hyper parameter optimization library Optuna to find the optimal hyper parameters. The experimental results are shown in Tables 1-3 and Figure 12. The last three columns of Tables 1-3 correspond to the differences in the three metrics between each algorithm and the proposed HHOCR. From the comparative experimental results in Tables 1-3 and Figure 12, it can be observed that among the seven algorithms, including feature-based methods (POP, BPR) and deep learning-based methods (GRU4 Rec, BERT4 Rec, Light-GCN, TP-GNN, IRS-GCNet), the proposed heterogeneous hypergraph, and attention-based online course recommendation algorithm (HHOCR) was compared and analyzed. The following conclusions were drawn:  
In the case of recommending the top 5 items, the HHOCR algorithm demonstrated the best performance, with NDCG@5 reaching 0.7965, F1-Score@5 at 0.6945, and Recall@5 at 0.7102. Compared to the second-ranked IRS-GCNet, HHOCR improved these three metrics by 0.0173, 0.0408, and 0.0510, respectively. Compared to other algorithms, HHOCR also significantly outperformed POP, BPR, GRU4 Rec, BERT4 Rec, and Light-GCN. This indicates that HHOCR has a clear advantage in accuracy and recall for the top 5 recommendations. In the case of recommending the top 10 items, the HHOCR algorithm still performed exceptionally well, with NDCG@10 reaching 0.8593, F1-Score@10 at 0.7813, and Recall@10 at 0.7514. Compared to the second-ranked IRS-GCNet, HHOCR improved by 0.0613, 0.0824, and 0.0724, respectively. Compared to other algorithms, HHOCR's NDCG@10 and F1-Score@10 were 0.5233 and 0.3939 higher than POP's, and 0.3736 and 0.2906 higher than BPR's, and 0.3179 and 0.3024 higher than GRU4 Rec's. This further validates HHOCR's superior performance in recommending the top 10 items. In the case of recommending the top 15 items, the HHOCR algorithm continued to perform excellently, with NDCG@15 reaching 0.8938, F1-Score@15 at 0.8092, and Recall@15 at 0.7698. Compared to the second-ranked TP-GNN, HHOCR improved by 0.0699, 0.0907, and 0.0509, respectively. Compared to other algorithms, HHOCR's NDCG@15 and F1  
Figure 12: Comparison Chart of Three Metrics: NDCG, F1-Score, and Recall. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **NDCr@15** & **F1-Score@15** & **Recall@15** & \begin{tabular}{c} **Diff.** \\ **NDCG@15** \\ \end{tabular} & \
\begin{tabular}{c} **Diff.** \\ **F1-Score@15** \\ \end{tabular} &
\begin{tabular}{c} **Diff.** \\ **Recall@15** \\ \end{tabular} \\ \hline POP & 0.4937 & 0.5126 & 0.5217 & \(-\)0.4001 & \(-\)0.2966 & \(-\)0.2481 \\ \hline BPR & 0.5189 & 0.5264 & 0.5313 & \(-\)0.3749 & \(-\)0.2828 & \(-\)0.2385 \\ \hline GRU4 Rec & 0.6343 & 0.5483 & 0.6095 & \(-\)0.2595 & \(-\)0.2609 & \(-\)0.1603 \\ \hline BERT4 Rec & 0.7184 & 0.659 & 0.6286 & \(-\)0.1754 & \(-\)0.1502 & \(-\)0.1412 \\ \hline Light-GCN & 0.8068 & 0.7079 & 0.6991 & \(-\)0.0870 & \(-\)0.1013 & \(-\)0.0707 \\ \hline TP-GNN & 0.8239 & 0.7185 & 0.7189 & \(-\)0.0699 & \(-\)0.0907 & \(-\)0.0509 \\ \hline IRS-GCNet & 0.8130 & 0.7093 & 0.7094 & \(-\)0.0808 & \(-\)0.0999 & \(-\)0.0604 \\ \hline HHOCR & 0.8938 & 0.8092 & 0.7698 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall Recommendation performance for k = 15. Score@15 were 0.4001 and 0.2966 higher than POP's, 0.3749 and 0.2828 higher than BPR's, and 0.2595 and 0.2609 higher than GRU4 Rec's. This indicates that HHOCR still has a significant advantage in recommending the top 15 items. Although POP, BPR, GRU4 Rec, BERT4 Rec, Light-GCN, TP-GNN, and IRS-GCNet performed well in certain metrics, they had limitations in capturing complex relationships and dynamic features.