# Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading
## 4 Experiments
### Comparison Experiments  
To evaluate the superiority of LGCNHS, we varied the length of the recommendation list (\(L=30,\ 50,\ 100\)) while keeping other parameters fixed and compared its performance with four related algorithms. The results demonstrate that LGCNHS outperforms the four benchmark algorithms in terms of both accuracy and diversity. For detailed results, refer to Tables 2-4. From the comparison of ProbS [4], HeatS [5], and HybridS [6], it is evident that HybridS significantly outperforms both ProbS and HeatS across different recommendation list lengths in terms of accuracy and diversity. This indicates that the linear fusion of these two spreading-based recommendation algorithms can effectively enhance the recommendation performance. Specifically, HybridS achieves a balance between accuracy and diversity in practical recommendation scenarios, demonstrating its advantage in balancing these two aspects. These results validate that HybridS not only accurately captures user preferences but also provides more diverse recommendations, thereby improving the limitations of spreading-based recommendation algorithms to a certain extent. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**MovieLens**} & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{4}{c}{**Diversity**} \\ \cline{2-7}  & **P@30** & **R@30** & **F1@30** & **NDCG@30** & **H@30** & **I@30** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Probs \\ Heats \\ HybridS \\ LightGCNHS \\ LGCNHS \\ \end{tabular} } & 0.052 & 0.227 & 0.085 & 0.054 & 0.455 & 0.464 \\  & 0.108 & 0.366 & 0.167 & 0.135 & 0.693 & 0.411 \\  & 0.116 & 0.395 & 0.179 & 0.146 & 0.756 & 0.403 \\  & 0.075 & 0.215 & 0.111 & 0.087 & 0.462 & 0.452 \\  & 0.128 & 0.420 & 0.196 & 0.159 & 0.840 & 0.382 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Douban** \\ \end{tabular} } & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{4}{c}{**Diversity**} \\ \cline{2-7}  & **P@30** & **R@30** & **F1@30** & **NDCG@30** & **H@30** & **I@30** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Probs \\ Heats \\ HybridS \\ LightGCNHS \\ LGCNHS \\ \end{tabular} } & 0.002 & 0.008 & 0.004 & 0.002 & 0.995 & 0.433 \\  & 0.002 & 0.008 & 0.004 & 0.002 & 0.995 & 0.440 \\  & 0.005 & 0.017 & 0.008 & 0.006 & 0.997 & 0.452 \\  & 0.002 & 0.007 & 0.003 & 0.002 & 0.776 & 0.109 \\  & 0.008 & 0.025 & 0.012 & 0.009 & 0.990 & 0.153 \\ \hline \end{tabular}
\end{table} 
Table 2: Compare the results of the test (@30). Figure 4: Hybrid spreading proportionality constant \(\lambda\) vs. the curve of each metrics for a recommendation list length of 30. Compared to ProbS, HeatS, and HybridS, LightGCN performs poorly across various metrics, indicating that LightGCN-based recommendation algorithms are inferior to spreading-based recommendation algorithm on these two datasets. Although LightGCN effectively capture relationships between users and items, spreading-based recommendation algorithms such as ProbS, HeatS, and HybridS demonstrate better performance in terms of recommendation accuracy and diversity when handling information dissemination and user interest modeling. This may be due to LightGCN's reliance solely on user-item latent embedding representations for recommendations, as well as its susceptibility to data sparsity. Consequently, on these datasets, spreading-based recommendation algorithms outperform LightGCN. The LGCNHS model, which integrates HybridS and LightGCN, demonstrates outstanding performance across various metrics on the MovieLens dataset, indicating its excellence in both recommendation accuracy and diversity. On the Douban dataset, LGC-NHS also performs exceptionally well in accuracy metrics, with its average Hamming distance comparable to other algorithms. Although its intra-similarity dose not reach the minimum value, LGCNHS demonstrates a significant advantage over ProbS, HeatS, and HybridS in this regard, indicating excellent improvement effects.