## 4 Experimentation and Results Analysis
### Ablation Experiments  
The dataset was used to compare a total of six variants, w/o g, w/o s, w/o cl, w/o vcl, w/o g+cl, and w/o s+cl, with the C-MBR unfolding ablation realizations, as a way of demonstrating that the C-MBR model has certain rationality in all parts of its construction. Detailed information is shown in Table 2.  
Upon reviewing Table 2, it becomes evident that taking comprehensive considerations for various types of behaviors may enhance the learning of user preferences. Combining sequence-based local structure cross-behavioral comparison learning tasks with graph-based global structure cross-behavioral comparison learning tasks can effectively enhance overall performance. These tasks primarily focus on capturing both the user's personal information and their global information to learn user representations, thereby shaping user interests and preferences at different levels. The comparative analysis between scenarios with and without certain components indicates that the cross-behavior comparison learning task contributes to model performance improvement. This suggests that multiple behaviors exhibited by the same user can better reflect their preferences, and there exists a degree of commonality among these behaviors, which can be fully explored to create more robust representations and address data sparsity issues. The comparative analysis of w/o g and w/o g+cl, w/o s and w/o s+cl, concluded that the cross-behavior comparison learning task is conducive to improving the model performance, which indicates that multiple behaviors unfolded by the same user can better reflect the user's preferences, and there is also a certain degree of commonality between the multiple behaviors of the same user, and its commonalities can be fully explored to form a better representation, and effectively solve  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Morph** & **Description of Partial Ablation** & **HR@10** & **NDCG@10** \\ \hline w/o g & Only the local structure module and the sequence-based & & 0.1560 & 0.0838 \\  & cross-behavioral comparison task are retained. & & 0.1574 & 0.0842 \\ w/o cl & Retain the global structure module, the local structure & & 0.1482 & 0.0790 \\ w/o vcl & Delete only the cross-structure comparison learning task. & 0.1618 & 0.0884 \\ w/o & Only the local structure module is retained and all & & 0.1483 & 0.0791 \\ g+cl &
\begin{tabular}{c} Retain only the global structure module and remove all \\ comparison learning tasks. \\ \end{tabular} & & 0.1492 & 0.0796 \\ C-MBR & / & & 0.1688 & 0.0911 \\ \hline \hline \end{tabular}
\end{table}
Table 2: C-MBR model: its construction in the various parts of the ablation study.  
the data sparsity problems. The comparative analysis of w/o vcl and C-MBR fully verifies the value of the cross-structure comparison learning task for performance improvement, and it can be said that the cross-structure comparison learning task aligns the global structure representation information and the local structure representation information, which is conducive to obtaining the commonality of the interaction behaviors of users and projects with different structures, and strengthens the quality of structure modeling.