# Zero-Shot Recommendation AI Models for Efficient Job-Candidate Matching in Recruitment Process
## 3 Results and Discussion
### Comparison with Random Baseline Model  
This subsection provides a comparative analysis between the random baseline model (as described in Table 6) and the pretrained models evaluated using cosine similarity and dot product. The random baseline model serves as a benchmark to assess the effectiveness of the embedding models in the task of job-candidate matching.  
Below is a comparative analysis between the random baseline model and the embedding models evaluated using cosine similarity and dot product for specific Top-K accuracies (Top 1, Top 100, Top 200, Top 300, Top 400, and Top 500). The random baseline model, which selects candidates randomly, serves as a benchmark to assess the effectiveness of the embedding models in the task of job-candidate matching.  
Top 1 Accuracy: The random baseline model has a Top-1 accuracy of 0.04%, as shown in Table 6. In contrast, the best-performing model in Tables 4 and 5, 'all-MiniLM-L6-v2'  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Top1** & **Top50** & **Top100** & **Top150** & **Top200** & **Top250** & **Top300** & **Top350** & **Top400** & **Top450** & **Top500** \\ \hline
0.04 & 2.06 & 4.11 & 6.17 & 8.22 & 10.28 & 12.34 & 14.39 & 16.45 & 18.5 & 20.56 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Top-K Accuracy percentage of random baseline model across various rankings.  
with a vector length of 512, achieved a Top-1 accuracy of 3.35%. This stark difference highlights the superior capability of the 'all-MiniLM-L6-v2' model in accurately identifying the most suitable candidate for a job position on the first attempt.  
Top 100 Accuracy: For Top 100 accuracy, the random baseline model's performance is significantly lower, with an accuracy of 4.11%. Meanwhile, the 'all-MiniLM-L6-v2' model demonstrates a much higher accuracy, reaching 55.45% when using cosine similarity, indicating its effectiveness in narrowing down the pool of candidates to the top 100 most relevant ones.  
Top 200 Accuracy: The random model achieves a Top-200 accuracy of 8.22%, whereas the 'all-MiniLM-L6-v2' model significantly outperforms this with an accuracy of 67.36% (using cosine similarity), showcasing its ability to effectively identify a broader pool of suitable candidates.  
Top 300 to Top 500 Accuracies: As we extend the comparison to Top 300, Top 400, and Top 500 accuracies, the random baseline model's accuracies are 12.34%, 16.45%, and 20.56% respectively. In comparison, the 'all-MiniLM-L6-v2' model maintains a high level of accuracy, achieving 74.41% for Top 300, 76.76% for Top 400, and 81.11% for Top 500 when using cosine similarity. This demonstrates the model's consistent performance even when considering a large number of top candidates.  
In conclusion, the comparison with the random baseline model underscores the significant improvement in job-candidate matching accuracy achieved by the 'all-MiniLM-L6-v2' model. The substantial differences in accuracy for Top 1, Top 100, Top 200, Top 300, Top 400, and Top 500 highlight the model's effectiveness in accurately matching candidates to job descriptions, far surpassing the random selection approach.