# Generating Job Recommendations Based on User Personality and Gallup Tests
## 1 Introduction
Throughout this paper, we use the term _traits_ to refer to the 34 distinct themes identified by the CliftonStrengths assessment, which Gallup often refers to interchangeably as talents or strengths depending on the context. We do not use traits in the broader sense defined by psychological models like the Big Five. To avoid confusion between commonly used psychological constructs and the terminology of the CliftonStrengths framework, we clarify the following distinctions. In this work we use the following terms:  
* _Personality traits_ refer to broad, stable dispositions that influence behavior across situations. This includes models like the Big Five (e.g., extraversion, conscientiousness), which provide a general psychological profile. * _Strengths_ are defined, following the Gallup CliftonStrengths framework, as a person's consistent near-perfect performance in a specific activity. Strengths emerge when innate talents are refined through practice and knowledge. * _Talents_ are naturally recurring patterns of thought, feeling, or behavior that can be productively applied. They represent the raw potential that, when invested in, develops into a strength. By incorporating the Gallup test's insights into job matching, our method seeks to provide a more holistic and personalized approach to recruitment. This integration can help ensure that job seekers are matched with roles that not only align with their skills and qualifications but also resonate with their natural strengths and talents. Ultimately, this novel approach has the potential to enhance career satisfaction, productivity, and long-term job retention by creating a better fit between individuals and their work environments. This paper makes several key contributions to the field of job recommendation systems. Firstly, it introduces a unique approach for incorporating personality traits, as evaluated by the Gallup CliftonStrengths assessment, into job recommendation systems. This framework provides a more nuanced approach to matching candidates with job opportunities by considering their natural strengths and talents. In contrast to traditional methods that rely on manual trait-job pairing, our work employs GPT-4 to dynamically infer rankings, adapting to evolving job markets. In addition, it applies a bias detection/mitigation technique to the LLM-based results to ensure fairness across demographics. Secondly, the paper validates the effectiveness of the proposed method through empirical experiments, demonstrating how integrating personality traits can improve the accuracy and relevance of job recommendations. It integrates different distance metrics, including a sigmoid-weighted Manhattan distance to prioritize mismatches in high-impact traits. Last and most importantly, it incorporated the proposed approach in an actual job recommendation platform in Kazakhstan that is used by several companies and hundreds of candidates. The paper is structured to provide a comprehensive overview of our novel method for integrating personality traits into job recommendation systems. Following this introduction, Section 2 discusses related work and the role of personality in job recommendations, highlighting the importance of personalization and the limitations of existing systems. Section 3 presents the proposed method, including data processing and model design, detailing how the Gallup CliftonStrengths assessment is integrated into the recommendation framework. Section 4 details the experimental setup and provides an overview of the application that currently supports our approach. Section 5 presents the results, providing an analysis of the effectiveness of our approach compared to traditional methods. Finally, Section 6 concludes the paper and outlines future work, discussing potential extensions and applications of our framework in broader recruitment contexts.