# Zero-Shot Recommendation AI Models for Efficient Job-Candidate Matching in Recruitment Process
## 1 Introduction
By assessing their performance against traditional AI models and human recruiters, we seek to demonstrate their potential in overcoming current challenges in talent acquisition. This research contributes to the evolving field of AI in recruitment, offering insights into how Zero-Shot Learning can lead to more efficient, unbiased, and adaptable recruitment processes. With the advent of advanced AI technologies, the recruitment process is undergoing a significant transformation. The integration of AI-driven solutions, such as Zero-Shot Recommendation AI models, promises to revolutionize job-candidate matching, making it more efficient and unbiased. This paper explores these pretrained models and their potential to enhance the recruitment process. Recent developments in e-recruitment have led to an increase in online job descriptions and a surge in job seekers submitting resumes, creating a massive pool of data [13]. This abundance of information necessitates efficient job-candidate matching processes, where recommender system technology plays a crucial role. Semantic technologies, in particular, have shown promise in improving e-recruitment by guiding document processing and automatic matching, thus enhancing job recommendation results [13]. Another approach involves using college-specific online job board systems, which aid students in finding suitable job opportunities [14; 15; 16]. These systems offer personalized job suggestions based on student abilities and assist businesses in candidate matching, demonstrating the potential of targeted job recommendation systems. The concept of hybrid filtering in job recommendation systems has also been explored. By combining user and company datasets, these systems match user profiles with appropriate companies using various recommendation algorithms [17; 18; 19; 20]. This approach, which includes content-based, collaborative, and hybrid filtering, addresses the limitations of individual methods, offering a more comprehensive solution. However, a major concern in recruitment recommendation domains is the Matching Scarcity Problem (MaSP), where candidates or job vacancies suffer from a lack of matching opportunities [21; 22; 23; 24]. Strategies to identify and mitigate MaSP involve introducing changes in curricula and job descriptions to approximate candidates to semantically related jobs, thereby reducing the number of CVs and jobs suffering from MaSP. In light of these developments, Zero-Shot Recommendation AI models emerge as a promising solution to the challenges faced in the recruitment process. These models, capable of making accurate predictions or recommendations without explicit training on specific tasks, are particularly valuable in scenarios with limited or non-existent data, such as newly created roles or unique skill sets [25]. The ability of Zero-Shot Learning to generalize from learned information to new, unseen tasks holds the potential to significantly enhance recruitment processes, making them more agile and inclusive. Recent advancements in NLP and AI have led to the development of more sophisticated job recommendation systems. For instance, a study by [PERSON] [26] explores the use of NLP in recommender systems, specifically in the domain of online recruitment. This approach involves analyzing resumes, profiles, and job descriptions to improve the matching process, addressing the \"cold start\" problem where new job postings and candidate profiles are not adequately matched. Another innovative approach is the job recommendation method based on attention layer scoring characteristics and tensor decomposition, as proposed by [PERSON] et al. [27]. This method focuses on users' attention levels and interactive behaviors, offering a more nuanced understanding of job seekers' interests and preferences. [PERSON] et al. [28] introduced a bi-directional recommendation system that supports both recruiters and job seekers.