# Zero-Shot Recommendation AI Models for Efficient Job-Candidate Matching in Recruitment Process
## 1 Introduction  
The recruitment process, a critical component of human resource management, has undergone significant transformations in the digital age. Traditional methods of talent acquisition, often time-consuming and dependent on subjective human judgment, are increasingly being supplemented and, in some cases, replaced by artificial intelligence (AI)-driven solutions. This paper delves into an innovative aspect of this technological advancement: Zero-Shot Recommendation AI models, which promise to revolutionize the efficiency of job-candidate matching in recruitment. This work represents a preliminary pilot approach to the problem, with fine-tuning and further refinement planned as the next stages of our research. By adopting a phase-wise methodology, we aim to iteratively enhance the model's accuracy and reliability, tailoring it more closely to the nuances of the recruitment domain. Recruitment is an intricate process, balancing the need for speed with the necessity for accurate and fair candidate assessment. Traditional methods have struggled to keep pace with the rapidly changing job market, where new roles emerge continuously, and the skills required for existing roles evolve. Moreover, the sheer volume of job applications for positions, especially in populous sectors, can overwhelm human resources, leading to inefficiencies and potential biases in candidate selection. These challenges underscore the need for an intelligent, adaptive, and efficient recruitment process. The recruitment process, encompassing the analysis of job descriptions, candidate CV screening, shortlisting, interviews, and job offers, can be partially automated with the aid of Artificial Intelligence (AI) solutions. These systems, known as Job Recommendation Systems (JRS) [1; 2; 3], are designed to support these processes. Among the most prevalent types of JRS are Content-Based JRS (CB-JRS), Collaborative Filtering JRS (CF JRS), Hybrid JRS (H-JRS), and Knowledge-Based JRS (KB JRS) [4; 5]. Each type has distinct characteristics and methodologies for recommending jobs to users:  
* Content-Based JRS (CB-JRS): These systems recommend jobs to users based on the description of the jobs and a profile of the user's preferences. The recommendation is made by matching the content of the job postings, such as required skills, experience level, and job roles, with the user's profile, which includes their skills, past experiences, and preferences. This approach assumes that jobs similar to those a user liked in the past will also be of interest. * Collaborative Filtering JRS (CF JRS): CF JRS recommend jobs based on the past behavior of users and similar decisions made by other users. This method does not require job content but relies on user-job interactions. The underlying assumption is that users who agreed in the past will agree in the future, and thus, jobs liked by similar users are recommended. * Hybrid JRS (H-JRS): Hybrid systems combine both content-based and collaborative filtering approaches to leverage the advantages of both. By integrating these methodologies, H-JRS can provide more accurate and relevant job recommendations. This approach helps to mitigate the limitations of each system when used independently, such as the cold start problem in collaborative filtering and the limited content analysis in content-based systems. * Knowledge-Based JRS (KB JRS): These systems recommend jobs based on explicit knowledge about users and jobs. Unlike other types, KB JRS do not solely rely on users' past behavior but use a knowledge base that includes rules and constraints to match users with suitable jobs. This approach is particularly useful when there is a need to consider complex requirements and preferences that are difficult to capture through user-job interactions alone. Current Job Recommendation Systems (JRS) exhibit inherent limitations that significantly impact their effectiveness in dynamic recruitment environments.