# Zero-Shot Recommendation AI Models for Efficient Job-Candidate Matching in Recruitment Process
## 3 Results and Discussion
### Analysis of Embedding Models Using Dot Product
** & **Top1** & **Top50** & **Top100** & **Top150** & **Top200** & **Top250** & **Top300** & **Top350** & **Top400** & **Top50** & **Top500** \\ \hline LaBSE & 512 & 1.85 & 26.66 & 38.28 & 45.90 & 52.67 & 66.66 & 59.52 & 63.36 & 66.07 & 68.21 & 70.49 \\ LaBSE & 768 & 1.92 & 27.58 & 36.92 & 44.83 & 51.75 & 56.99 & 60.51 & 63.44 & 65.72 & 67.78 & 69.21 \\ LaBSE & 1024 & 1.85 & 26.02 & 35.42 & 42.55 & 48.61 & 54.60 & 58.30 & 61.01 & 63.97 & 65.36 & 67.43 \\ \hline all-Mnist-M-12\(\times\)2 & 512 & 2.71 & 38.77 & 51.10 & 58.30 & 63.72 & 67.14 & 70.21 & 72.42 & 74.70 & 76.62 & 78.55 \\ all-Mnist-M-12\(\times\)2 & 768 & 3.21 & 37.35 & 50.25 & 57.31 & 63.08 & 67.07 & 70.35 & 72.13 & 74.20 & 75.91 & 77.76 \\ all-Mnist-M-12\(\times\)2 & 1024 & 3.42 & 37.06 & 49.68 & 57.45 & 62.37 & 66.57 & 69.78 & 72.42 & 73.70 & 75.84 & 77.33 \\ \hline all-Mnist-M-16\(\times\)2 & 512 & 3.35 & 40.56 & 53.96 & 61.44 & 67.14 & 71.92 & 74.55 & 76.48 & 78.33 & 79.47 & 80.97 \\ all-Mnist-M-16\(\times\)2 & 768 & 3.42 & 40.48 & 55.45 & 62.72 & 63.76 & 71.13 & 74.41 & 76.76 & 78.55 & 79.69 & 81.11 \\ all-Mnist-M-16\(\times\)2 & 1024 & 3.28 & 40.13 & 52.96 & 60.66 & 66.29 & 69.99 & 72.49 & 75.12 & 78.91 & 78.90 & 79.69 \\ all-mpreet-base-v2 & 512 & 2.99 & 36.64 & 48.75 & 57.99 & 63.44 & 69.93 & 70.35 & 72.42 & 74.06 & 75.12 & 76.98 \\ all-mpreet-base-v2 & 768 & 2.92 & 36.42 & 48.40 & 55.81 & 63.37 & 65.72 & 69.71 & 77.42 & 74.13 & 75.12 & 76.84 \\ all-mpreet-base-v2 & 1024 & 3.56 & 36.85 & 49.04 & 57.16 & 62.08 & 66.00 & 68.85 & 71.49 & 73.63 & 74.98 & 76.27 \\ \hline distitiuse-base-mulligingual-cased-v2 & 512 & 2.92 & 33.78 & 46.76 & 55.02 & 60.51 & 64.88 & 68.00 & 70.28 & 73.34 & 75.77 & 77.05 \\ distitiuseuse-mulligingual-cased-v2 & 768 & 2.92 & 35.20 & 46.04 & 53.96 & 99.66 & 60.01 & 67.28 & 69.78 & 72.06 & 74.06 & 75.27 \\ distitiuseuse-mulligingual-cased-v2 & 1024 & 2.28 & 31.36 & 43.12 & 50.53 & 56.81 & 60.58 & 64.43 & 67.36 & 66.92 & 72.49 & 73.70 \\ \hline msmmmostre-distitivel-cos*v5 & 512 & 2.49 & 30.51 & 42.91 & 51.32 & 57.31 & 62.37 & 64.72 & 67.28 & 69.92 & 71.42 & 73.41 \\ msmmarco-distitivel-cos*v5 & 768 & 2.64 & 32.93 & 43.91 & 50.46 & 57.52 & 61.87 & 64.58 & 67.00 & 69.35 & 70.49 & 72.56 \\ msmarco-disConversely, the 'paraphrase-multilingual-MiniLM-L12-v2' model, especially with a vector length of 1024, showed the lowest Top-1 accuracy, at only 1.35%. While its performance improves with higher K values, it reaches 69.71% at Top-500, which is still lower compared to other models. This model's relatively lower accuracy across all K values suggests it is less effective for this specific application of job-candidate matching. In general, as the Top-K value increases, all models demonstrate an improvement in accuracy. However, the 'all-MiniLM-L6-v2' model stands out for its consistently high performance, making it the preferred choice for practical applications in recruitment. In contrast, the 'paraphrase-multilingual-MiniLM-L12-v2' model, particularly at higher vector lengths, underperforms in comparison, indicating it may be less suited for scenarios requiring high precision in candidate matching.