# Generating Job Recommendations Based on User Personality and Gallup Tests
## 2 Related Work
According to another recent survey [13], deep learning techniques in job recommendation systems include Convolutional Neural Networks (CNNs) for extracting features like keywords from job descriptions, Recurrent Neural Networks (RNNs) for analyzing sequential text data to predict user preferences, Autoencoders for creating compact representations of job descriptions to identify similar roles, and Generative Adversarial Networks (GANs) for generating new job descriptions aligned with user interests, offering sophisticated, personalized, and adaptable recommendations at the cost of higher computational and training complexity. Finally, Graph Neural Networks (GNNs) combined with knowledge-graph-based techniques [14] are gaining hype for providing better skills, experience, and education matching between candidates and positions. Recommending a job that matches the user's profile is very important since it can reduce underemployment or unemployment and, indirectly, contribute to social and economic development. In this direction, the work of [7] presents an interesting approach that employs Large Language Models and, more specifically, BERT to generate content-based job recommendations based on user resumes and job descriptions. Simpler NLP techniques have been employed in [15] to find matching between user profiles and job descriptions. However, these approaches stick to the matching of hard skills and ignore soft skills and personality traits that can either be hidden behind the words of a job description text or a user resume. The importance of soft skills is recognized by the CareProfSys system [16], which collects demographic and personality data from users, along with skill information from the user's CV and professional social media (i.e., LinkedIn). Manual extraction of text segments from the above sources is performed, and rule-based and BERT-based mapping is employed to map texts into specific skill codes from a manually compiled lexicon. Some filtering steps limit the candidates that match each job, and finally, an ontology is employed to perform the matching between the positions' requirements and the candidate profiles. Another job recommendation engine, JobFit [17], processes job descriptions and applicants' profiles and generates a JobFit score indicating how well the applicant fits a position. Once again, the job inputs comprise required skills, experience, and qualification, and while experience and qualification act as filters, skills are matched to the applicant's skills using NLP techniques. The system also considers the applicant's personality (the big 5 traits) by measuring the correlation of the user's personality with the personality of employees who work in this job and by using it to measure the possible satisfaction from the job. Prior work on personality-aware job recommendations (e.g., [17]) often relies on the Big Five model, which, while useful for broad trait alignment, lacks granularity for specific strength-based matching. In contrast, Gallup's 34 themes--such as 'Achiever', 'Communication', or 'Strategic'--provide actionable insights into how innate talents align with role-specific demands (e.g., 'Strategic' for consulting roles). This fine-grained approach bridges a gap left by broader psychological models and manual skill-mapping methods (e.g., [16]), enabling dynamic, scalable recommendations. In recent years, transparent and explainable recommender systems have attracted considerable attention. [PERSON] et al. 's TriRank [18] models aspect-level interactions in a tripartite graph to enable users to inspect, which review-derived aspects drive each recommendation, thus improving transparency and scrutability. [PERSON] et al. 's GREASE [19] generates both factual and counterfactual explanations for GNN-based recommenders by optimizing adjacency perturbations, offering concise, user-understandable reasons for item rankings. More recently, [PERSON] et al.