# Zero-Shot Recommendation AI Models for Efficient Job-Candidate Matching in Recruitment Process
## 2 Materials and Methods  
### Dataset  
The data source for constructing the various AI models comprises anonymized resumes in PDF and Word formats, obtained from potential candidates, as well as job specifications from Avenga. Currently, the resumes are limited to the English language only. Given the specific requirements of the models, both the resumes and job specifications have been segmented into chunks of lengths 512, 768, and 1024. These chunk lengths have been chosen for analysis to accommodate the models' processing capabilities and to ensure a comprehensive evaluation of their performance in matching candidates with job specifications.  
Additionally, the dataset includes historical mappings of job-to-candidate pairings, which have been manually created by recruiters. It is important to note that not all resumes in the database, which were used to calculate the accuracy metric, have been reviewed by the recruiters. This lack of comprehensive review may lead to a significant overestimation of the error rate in the model's performance evaluations. The presence of manually curated job-candidate mappings serves as a valuable reference point; however, the potential for an inflated error due to unreviewed resumes must be taken into consideration when interpreting the results.  
The dataset provides a comprehensive overview of the recruitment ecosystem, particularly emphasizing the IT sector and related technical fields. This focus reflects the dynamic nature of the tech industry, where the demand for specialized skills is both high and rapidly evolving. The predominance of IT-related job offers in our dataset highlights the sector's significant role in the current job market and underscores the dataset's utility in analyzing recruitment trends within this vital economic activity sector. Table 1 summarizes the key aspects of this analysis.  
The dataset includes a total of 2432 unique candidates and 1403 unique job positions. This diversity reflects the variety of specializations and roles in the contemporary job market. A significant portion of the dataset (738 instances) comprises scenarios where one job is matched with exactly one candidate. This one-to-one correspondence is indicative of highly specialized roles or unique candidate profiles that perfectly fit specific job descriptions.  
On the other hand, there are 2431 instances where a single job is matched with multiple candidates. This scenario is common in job positions with broader or more general requirements, where a larger pool of candidates may possess the necessary qualifications or skills. Similarly, there are 1246 instances where a single candidate is matched with multiple job positions. This reflects the versatility of some candidates who may fit into various roles due to their diverse skill sets or adaptable profiles.  
In total, the dataset contains 3169 records of job-candidate matches, providing a rich source for analysis and model training. The distribution of these matches (Table 2) offers insights into the dynamics of the job market and the versatility of the job-seeking population. This comprehensive dataset forms the basis of our subsequent analysis, wherein we apply Zero-Shot Recommendation AI models to enhance the efficiency and accuracy of job-candidate matching.  
\begin{table}
\begin{tabular}{l l l} \hline
**Category** & **Count** & **Description** \\ \hline Unique Candidates & 2432 & Number of unique candidates in the dataset \\ Unique Jobs & 1403 & Number of unique jobs in the dataset \\
1 Job–1 Candidate & 738 & Number of matches where one job is matched with exactly one candidate \\
1 Job–Many Candidates & 2431 & Number of matches where one job is matched with multiple candidates \\
1 Candidate–Many Jobs & 1246 & Number of matches where one candidate is matched with multiple jobs \\ \hline Total Records & 3169 & Total number of job–candidate matches in the dataset \\ \hline \end{tabular}
\end{table}
Table 1: Summary of job–candidate matching analysis.