# SoftRec: Multi-Relationship Fused Software Developer Recommendation
## 4 Experiment and Discussion
### Experimental Results and Discussions
#### 4.2.3 Effects of the Multi-Relationship (RQ3)  
In SoftRec, the parameters \(\lambda_{C}\) and \(\lambda_{S}\) determined the effects of developer-developer collaboration relationship and task-task association relationship on the recommendation results. Now let us discuss how these relationships affect SoftRec and how to determine their values. From Equations (8)-(10) we can see that SoftRec uses \(\lambda_{C}\) and \(\lambda_{S}\) to balance the collaboration relationships and the association relationships with the developer-task interaction relationships. When \(\lambda_{C}=0\), it is equivalent to completely ignoring the influence of collaboration relationships. As \(\lambda_{C}\) increases, it means that the collaboration relationships are leveraged to make recommendations with a higher priority. We also  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**GitHub Dataset**} & \multicolumn{3}{c}{**GitLab Dataset**} \\ \cline{2-7}  & **Precision** & **Recall** & **ndcg** & **Precision** & **Recall** & **ndcg** \\ \hline PR-CF & 0.3791 & 0.7154 & 0.3513 & 0.3799 & 0.7299 & 0.4135 \\ \hline IR+CN & 0.3248 & 0.6411 & 0.3361 & 0.3145 & 0.6377 & 0.3789 \\ \hline DMF & 0.3399 & 0.6998 & 0.3205 & 0.3615 & 0.6819 & 0.4106 \\ \hline NFM & 0.3413 & 0.6918 & 0.3327 & 0.3369 & 0.7311 & 0.3704 \\ \hline SoftRec & 0.4673 & 0.7633 & 0.4727 & 0.4929 & 0.7701 & 0.5109 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different approaches (\(k=5\)).  
demonstrate it in our experiments, as shown in Figure 4,b, as \(\lambda_{C}\) and \(\lambda_{S}\) increases from 0.01 to 5 and 0.01 to 10, SoftRec's precision, recall and ndcg values increase, while when \(\lambda_{C}\) and \(\lambda_{S}\) exceed 5 and 10 respectively, these values decrease gradually.  
Furthermore, to compare the performance of SoftRec with and without the multi-relationship, we set \(\lambda_{C}=\lambda_{S}=0\). Table 4 shows the compared results, where SoftRec' denotes the SoftRec without the multi-relationship. For example, for GitHub dataset, the values of precision, recall and ndcg of SoftRec with multi-relationship are 50.11%, 32.93% and 59.10% higher than the values of SoftRec without multi-relationship.  
This phenomenon presented in Figure 4 and Table 4 shows that by leveraging of the developer-developer collaboration relationships and task-task association relationships can help improve the recommendation accuracy effectively. Because the optimal values for those parameters are domain-specific, we set the parameters through trial-and-error in the experiments.  
Figure 4: Take GitHub dataset as an example, analyze the effect of developer–developer collaboration relationship and task–task association relationship on the performance by adjusting the parameters \(\lambda_{C}\) and \(\lambda_{S}\), where \(k=5\).  
Figure 3: Comparison of the recommendation accuracy (precision, recall and ndcg) with different density of developer–task interaction matrix (e.g., the density ranges from \(1*\rho_{1}\) to \(0*\rho_{1}\) in subfigure (a)) in GitHub and GitLab datasets.