# Zero-Shot Recommendation AI Models for Efficient Job-Candidate Matching in Recruitment Process
## 3 Results and Discussion
### Analysis of Embedding Models Using Dot Product  
The analysis of embedding models using the dot product metric, as detailed in Table 4, provides insightful conclusions regarding their performance in job--candidate matching. The values in the table represent percentages, indicating the accuracy of each model across various Top-K rankings. The 'all-MiniLM-L6-v2 model', featuring vector lengths of 512, achieved the highest observed Top-1 accuracy among the models evaluated, reaching 3.35%. While this marks a notable performance, it's important to contextualize this achievement within the broader landscape of Job-Recommender Systems (JRSs) approaches. The Top-1 accuracy figure, though the highest in this comparison, underscores the challenges inherent in accurately matching job descriptions with candidate profiles on the first attempt. This performance, while promising, suggests there is significant room for improvement and exploration of alternative or complementary approaches to enhance accuracy further. Moreover, the model's performance trend across higher K values, culminating in a Top-500 accuracy of 77.55%, reflects its ability to provide a wider range of potentially suitable candidate matches as more options are considered. This pattern of increasing accuracy with higher K values indicates the model's utility in casting a wider net for candidate--job matches, although it also highlights the inherent trade-offs between precision and recall in such systems. The results underline the importance of continued research and development in this field to advance the capabilities of JRS technologies. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline
**Model** & **Len. ** & **Top1** & **Top50** & **Top100** & **Top150** & **Top200** & **Top250** & **Top300** & **Top350** & **Top400** & **Top450** & **Top500** \\ \hline LaBSE & 512 & 1.71 & 26.23 & 36.64 & 44.83 & 80.25 & 65.31 & 58.45 & 61.65 & 64.58 & 67.36 & 68.78 \\ LaBSE & 768 & 2.00 & 26.16 & 38.13 & 45.69 & 50.32 & 54.31 & 57.45 & 60.01 & 63.15 & 65.07 & 67.78 \\ LaBSE & 1024 & 1.85 & 23.88 & 33.86 & 42.20 & 48.33 & 51.60 & 55.52 & 58.37 & 60.66 & 62.99 & 65.07 \\ \hline all-MnidM-L1-2\(\times\)2 & 512 & 2.99 & 35.71 & 46.19 & 52.96 & 58.30 & 62.01 & 64.93 & 67.78 & 70.49 & 72.70 & 74.13 \\ all-MnidM-L12\(\times\)2 & 768 & 2.78 & 35.42 & 45.19 & 51.89 & 56.59 & 60.44 & 62.87 & 65.93 & 68.78 & 71.13 & 73.34 \\ all-MnidM-M-L12\(\times\)2 & 1024 & 3.14 & 33.50 & 45.40 & 51.39 & 55.74 & 60.37 & 63.58 & 66.14 & 69.14 & 71.35 & 73.20 \\ \hline all-MnidM-L6\(\times\)2 & 512 & 2.85 & 37.28 & 49.25 & 56.45 & 61.15 & 68.57 & 71.35 & 74.13 & 75.91 & 77.55 \\ all-MnidM-L6\(\times\)2 & 768 & 3.28 & 36.92 & 51.03 & 58.23 & 63.36 & 67.50 & 69.57 & 71.49 & 74.88 & 75.69 & 77.41 \\ all-MnidM-L6\(\times\)2 & 1024 & 3.35 & 35.85 & 47.83 & 55.17 & 61.01 & 64.72 & 67.50 & 70.21 & 77.24 & 74.84 & 76.84 \\ all-mpreet-base-v2 & 512 & 2.42 & 31.86 & 44.19 & 52.67 & 59.09 & 63.15 & 65.08 & 68.78 & 70.71 & 72.27 & 73.84 \\ all-mpreet-base-v2 & 768 & 1.78 & 32.86 & 45.12 & 51.75 & 57.73 & 61.94 & 60.50 & 67.57 & 69.85 & 77.77 & 73.77 \\ all-mpreet-base-v2 & 1024 & 2.99 & 31.72 & 45.47 & 53.96 & 59.02 & 62.15 & 65.00 & 68.00 & 70.06 & 71.70 & 73.70 \\ \hline distitiuseuseuseuse-multilingual-cased-v2 & 512 & 2.42 & 33.64 & 44.12 & 50.82 & 56.66 & 61.44 & 65.00 & 68.57 & 71.49 & 73.84 & 76.13 \\ distitiuseuseuseuseuse-multilingual-cased-v2 & 768 & 2.14 & 31.22 & 41.48 & 49.68 & 55.10 & 60.09 & 63.72 & 66.50 & 69.00 & 71.72 & 73.13 \\ distiti  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline
**Model** & **Len.