# Generating Job Recommendations Based on User Personality and Gallup Tests
## 5 Results and Discussion  
The evaluation process engaged 100 university graduates who took the personality test. This section presents and discusses the main results of the evaluation process in a comparative manner, highlighting the overall performance of the method using different metrics and the respective performance in various user subgroups depending on the domain of studies. Specifically, the graduates evaluated three distinct job recommendation methods--Weighted Manhattan, Weighted Euclidean, and Baseline--by scoring each method on a scale from 1 to 10. The analysis focuses on comparing the performance of the three approaches by examining their score distributions across participants. The dataset also includes additional attributes such as the graduates' current specialty, the broader area of their work, their initial field of study, and whether their current job aligns with their studied specialty. The analysis that follows aims to uncover patterns, strengths, and weaknesses of each method across different graduate subgroups and provide actionable insights for improving job recommendation systems. This comparative assessment provides insights into the effectiveness of each approach in capturing meaningful patterns related to personality traits and professional alignment and satisfaction. The first comparison examines the performance of the two proposed alternatives versus the baseline method. As shown in Figure 11, which displays the average score and the standard deviation for each method, the two proposed alternatives outperform the baseline. The weighted Manhattan method is almost 0.5 degrees better than the baseline, on average, based on the evaluation of the 100 graduates. This means that the method is statistically significantly better than the baseline, almost at the 90% confidence interval. Following the overall performance comparison, we would like to examine the differences in the distribution of scores to the three methods between graduates who are working on the specialization they studied and those who do not. As shown in Figure 12 the scores are more dispersed for all metrics among graduates working outside their specialty (in  
Figure 11: The distribution of ratings per method for graduates who work in the specialization they studied and those who do not. terquartile range [IQR]: 3.5-6.5 for Manhattan, 3-6 for Euclidean) compared to those within their specialty (IQR: 6-8 for Manhattan, 5-7 for Euclidean). The average score for the two proposed methods is higher than the baseline. The group of graduates who work in the same domain that they studied has a more clear opinion of the quality of the results of each method and a more cohesive evaluation. Apart from a few outliers, they rank the weighted Manhattan-based alternative higher than the other two methods, giving scores that are either 7 or 8 out of 10. More specifically, in the within-speciality group, the weighted Manhattan method achieved a median score of 7.5 (IQR = 6-8), with 75% of ratings being 7 or higher. The weighted Euclidean method showed a slightly lower consensus with a median of 6 (IQR = 5-7). Baseline scores were consistently the lowest with a median of 4 (IQR = 3-5). In the outside-speciality group, both proposed methods outperformed the baseline (median scores of 5 vs. 3 respectively), but exhibited wider score distributions (Manhattan IQR = 3.5-6.5; Euclidean IQR = 3-6). The baseline method showed the poorest performance with a median of 3 (IQR = 2-4). The greater variance in scores for this group suggests more variability in how relevant the recommendations were perceived to be. These numerical results demonstrate that graduates working in their field of study showed both higher satisfaction and greater consensus in their evaluations, particularly favoring the weighted Manhattan approach. The wider score ranges among those working outside their specialty may reflect more diverse career trajectories or differing expectations from the recommendation system.