# A Multi-Behavior Recommendation Method for Users Based on Graph Neural Networks
## 4 Experiments
### Experimental Results and Analysis  
We evaluate the performance of all baseline methods on different datasets, and the results are shown in Table 1, which summarizes the following observations: The MK-GCN model in this article significantly improves the recommendation performance. This performance gap can be attributed to the effective personalized multi-behavior pattern modeling and the rich context information of user and item representations obtained under the meta-learning paradigm. Most studies ignore the different behavior habits of different users and simply assign different weights to different behaviors. In this paper, we learn user personalized behavior feature representations from interaction graphs according to user behavior habits.  
MK-GCN consistently achieves better performance than the baseline models, but these baseline models have different degrees of limitations. SR-GNN and ST-GCN models do not consider the specific operation behavior of users, and only model and extract features based on the products that users interact with. The NMTR model only models the cascading relationships between multiple types of interaction behaviors and cannot explore the high-order behavior dependencies in the interaction graph. The MATN model aggregates different types of behavior patterns by weighted summation, which cannot comprehensively capture the complex interdependence between different types of interaction behaviors.  
MK-GCN consistently obtains better performance than the baseline models, which all have different degrees of the SR-GNN and ST-GCN models and do not consider the specific operational behavior of the user and only model and extract features based on the goods that the user interacts with. The NMTR model only models the cascading relationships between multiple types of interactions and cannot explore the higher-order behavioral  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Taobao**} & \multicolumn{2}{c}{**Beibei**} & \multicolumn{2}{c}{**JDATA**} \\ \cline{2-7}  & **HR@10** & **NDCG@10** & **HR@10** & **NDCG@10** & **HR@10** & **NDCG@10** \\ \hline SR-GNN & 0.321 & 0.181 & 0.591 & 0.326 & 0.432 & 0.263 \\ ST-GCN & 0.347 & 0.206 & 0.609 & 0.343 & 0.452 & 0.285 \\ NGCF & 0.302 & 0.185 & 0.611 & 0.375 & 0.461 & 0.292 \\ NMTR & 0.332 & 0.179 & 0.613 & 0.349 & 0.481 & 0.304 \\ MATN & 0.354 & 0.209 & 0.626 & 0.385 & 0.489 & 0.309 \\ MBGCN & 0.369 & 0.222 & 0.642 & 0.376 & 0.463 & 0.277 \\ MK-GCN & 0.472 & 0.300 & 0.683 & 0.405 & 0.512 & 0.316 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall performance of the model on the Beibei, Taobao, and JDATA datasets.  
dependencies in the interaction graph. The MATN model aggregates different types of behavioral patterns through weighted summation, which cannot fully capture the complex interdependencies between different types of interactions.  
Furthermore, the comparison between MK-GCN and the multi-behavior graph neural model MBGCN demonstrates the proposed method's advantages of multi-behavior dependency modeling. Among the various baseline methods, it can be observed that, compared to other single-row-for-model recommendation methods that do not distinguish between intersection types, the injection of multi-behavior information into the recommendation framework (i.e., NMTR, MATN, MBGCN) into multi-behavior information improves the performance. This result confirms the role of exploring multi-behavioral patterns for recommendation improvement.