## 4 Analysis and Discussion of Experimental Results
### Discussion  
In Figures 10 and 11, both the average satisfaction rate and the normalized discounted cumulative gain (NDCG) of the JRSR job recommendation method, as proposed in this paper, outperform the other four recommendation methods. This robustly underscores the effectiveness and superiority of the JRSR approach in job recommendation. In contrast with the TF-C and MS methods, JRSR uses a keyword iterative calculation approach to determine the similarity between the resume text of college graduates and the job recruitment text. This methodology not only mitigates the problem of neglecting low-frequency keywords but also enhances inter-keyword cross-referencing, resulting in a more robust semantic matching of text similarity. The job recommendation method GERS recommends job selections for graduates by calculating the similarity of employment characteristics among graduates and suggesting the choices of neighboring graduates as pre-recommended items [35]. This method is a collaborative filtering recommendation approach, which narrows down the recommendation scope. However, it simplifies the calculation of similarity in employment characteristics among graduates. In contrast, the JRSR recommendation method, when calculating the similarity in employment characteristics among graduates, establishes a social network based on academic data from graduates. This approach not only effectively computes the similarity in employment characteristics among graduates but also addresses the system cold-start problem caused by a lack of employment data for college graduates. Compared with the recommendation method RSCF, which combines content-based and collaborative filtering approaches [44], the JRSR recommendation method adds dimensions of major matching and salary matching. By using multiple methods and dimensions, JRSR matches the preferences and job requirements of college graduates, thereby enhancing the reciprocity of job recommendations.  
The empirical evaluation demonstrates that the job recommendation method introduced in this study exhibits superior performance relative to the benchmark method. Nonetheless, job recommendation for college graduates is a multifaceted and applied discipline, and this study acknowledges certain limitations. (a) While the semantic keyword  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & NDCG@5 & NDCG@10 & NDCG@15 & NDCG@20 \\ \hline TF-C & 0.638 & 0.641 & 0.612 & 0.591 \\ MS & 0.692 & 0.662 & 0.659 & 0.658 \\ GERS & 0.804 & 0.725 & 0.698 & 0.701 \\ RSCF & 0.791 & 0.751 & 0.759 & 0.741 \\ JRSR & 0.881 & 0.858 & 0.862 & 0.816 \\ \hline \hline \end{tabular}
\end{table}
Table 6: NDCG assessment values for different recommended list lengths.  
Figure 15: Analysis of NDCG evaluation values for different recommended list lengths.  
iterative algorithm enhances the accuracy of text similarity calculations, its iterative nature results in time-consuming processing with large datasets. Future research should, therefore, focus more on improving the scalability of this computational method. (b) The dataset used in this study is not diverse, which limits the consideration of data variability. Consequently, future work will aim to increase the sample size to broaden the assessment indicators. (c) This paper focuses on the two most prevalent employment characteristics, major and salary; future studies should encompass a broader range of employment attributes. (d) This study does not utilize a range of career assessment tools to aid graduates in gaining a more objective and comprehensive understanding of their employment aspirations. Future research could incorporate ideological education to enhance graduates' awareness of their career intentions, thereby improving the overall quality of job recommendations.