# Generating Job Recommendations Based on User Personality and Gallup Tests
## 3 Proposed Approach  
The proposed approach has been designed to generate job recommendations for graduate students who perform the Gallup test in order to evaluate their skills and help them find the most appropriate jobs out of a wide range of positions. The input it considers is the graduates' evaluation using the CliftonStrengths test and a text that describes each job position. The overall process is depicted in Figure 1.  
The first step in the proposed approach is to utilize the GPT-4 model to process job positions within specific sectors of a broader industry and identify the most important personality traits from the 34 official CliftonStrengths themes. This is achieved by constructing a precise and context-aware prompt (see Figure 2) designed to simulate the role of a Career Counselor. The prompt incorporates detailed information about the job title, sector, and industry, asking the AI to rank the CliftonStrengths themes from most relevant to least relevant for the specific role. The ranking process considers the job's specific requirements, necessary personality traits, and the strengths typically associated with success in the given position. By iteratively evaluating each of the traits in a step-by-step manner, the AI provides a ranked list in a standardized format, ensuring that every skill is used exactly once. This ranking is then used to assign a weight to each of the 34 CliftonStrengths traits, reflecting their relative importance for the given role. These weighted traits serve as the foundation for further processing and analysis in the workflow. Figure 1: The main flow of data in the proposed approach. Figure 2: The GPT-4 prompt generates the importance of each personality trait for each job title. The next step is to evaluate whether the LLM-based extraction process introduces any bias in the weighting of CliftonStrengths traits across all job positions. To achieve this, we construct a 34 \(\times\) 34 matrix, where each element represents the cumulative weight of a specific trait across all analyzed job positions. This matrix provides a comprehensive overview of how each trait is ranked and weighted on a global scale. By analyzing the matrix, we identify cases where certain traits have a total weight that exceeds a predefined threshold, indicating a potential bias introduced by the LLM-based ranking process. For such traits, a normalization function is applied to adjust the weights, smoothing out the overemphasis and ensuring that the distribution of trait importance aligns more evenly across the dataset. Let \(M\in\mathbb{R}^{34\times 34}\) be the cumulative weight matrix, where \(M_{i,j}\) is the total weight of trait \(i\) across all job positions. We first compute, for each trait \(i\),  
\[ \begin{split}& m_{i}=\max_{1\leq j\leq 34}M_{i,j},\\ & n_{i}=\min_{1\leq j\leq 34}M_{i,j}.\end{split}  \tag{1}\]  
We then identify any trait \(i\) for which \(m_{i}>100\) (our chosen bias threshold). For these \"overemphasized\" traits, we apply a min-max rescaling to cap the maximum at 100:  
\[M^{\prime}_{i,j}=\left\{ \begin{aligned} &\frac{M_{i,j}-n_{i}}{m_{i}-n_{i}} \times 100&\text{if }m_{i}>100,\\ & M_{i,j}&\text{otherwise}.\end{aligned} \right. \tag{2}\]  
This transformation ensures \(\max_{j}M^{\prime}_{i,j}=100\) and \(\min_{j}M^{\prime}_{i,j}=0\), preserving relative differences while smoothing extreme peaks. We selected simple min-max scaling for its transparency and ease of reproduction and because it retains monotonicity--ensuring that more important traits remain more heavily weighted even after correction. The impact of this normalization process is visualized in two plots (see Figure 3): the \"before\" figure, which illustrates the raw cumulative weights of the traits, and the \"after\" figure, which shows the adjusted weights following normalization. These visualizations provide insight into the effectiveness of the bias correction process and ensure that the workflow maintains a fair and balanced assessment of the CliftonStrengths traits.