# Generating Job Recommendations Based on User Personality and Gallup Tests
## 6 Conclusions and Future Work  
This study introduced a novel job recommendation system that integrates personality traits evaluated through the Gallup Clifton-Strengths assessment, offering a more holistic approach to career matching by aligning users' natural strengths with job requirements. Empirical evaluations involving 100 graduates demonstrated that the proposed methods significantly outperformed the baseline text-similarity approach, showing a 0.5-point average improvement in user satisfaction. Notably, graduates working in their studied specializations exhibited more cohesive evaluations, consistently favoring the proposed approach, while the performance of the two proposed variants varied across domains. These results underscore the value of incorporating personality traits into recommendation systems, particularly for enhancing alignment between users' intrinsic strengths and job roles. However, the study has several limitations. First, the sample size, though sufficient for initial validation, limits generalizability to niche specializations (e.g., underrepresented groups in IT or Natural Sciences). Second, reliance on self-reported scores introduces potential subjective bias, and the analysis focused narrowly on specialty alignment rather than demographic or job-market dynamics. Future work should expand the participant pool to include diverse specializations and geographic regions to validate robustness. Testing advanced similarity methods, such as machine learning-driven metrics or hybrid models integrating contextual job-market data, could further refine accuracy. As part of future work, we plan to conduct a rigorous ablation study to isolate and quantify the impact of each pipeline component--especially the Clifton-Strengths-derived personality vectors--on recommendation performance. In addition, we plan to incorporate comparisons with other recommendation techniques, such as collaborative filtering and hybrid recommenders, to further validate our approach in a broader algorithmic context. We also aim to explore the use of standard evaluation metrics, including AUC and precision/recall, to provide a more comprehensive view of accuracy and performance trade-offs. Furthermore, recognizing the importance of adaptability in both individuals and systems, we aim to investigate how dynamic, future-oriented strengths--such as those captured by the Gallup Adaptability, Learner, and Strategic themes--can be leveraged to match users with roles that are themselves evolving. This may include hybridizing narrow strength-based models with broader frameworks like the Big Five to better address the multidimensional nature of jobs that demand both technical precision and socio-emotional intelligence. Incorporating objective metrics like job retention rates or employer feedback, alongside longitudinal studies, would strengthen validity. For this reason, we plan to conduct a longitudinal follow-up incorporating objective indicators such as job placement outcomes, employer feedback, and career retention rates, to more robustly evaluate the long-term impact of the proposed method. Additionally, explainability frameworks could clarify why certain methods excel in specific domains. By addressing these challenges, personality-based recommendation systems can evolve into indispensable tools for fostering career satisfaction and talent retention. **Author Contributions:** Conceptualization, S.S., A.K. and C.T. ; methodology, A.K. and C.S. ; software, A.K. ; validation, S.S., A.K. and C.S. ; formal analysis, S.S. and Y.K. ; investigation, I.V. ; resources, S.S. and A.K. ; data curation, S.S. and A.K. ; writing--original draft preparation, S.S., C.S. and I.V; writing--review and editing, C.S. and I.V. ; visualization, A.K. and I.V. ; supervision, I.V. ; project administration, I.V. ; funding acquisition, B.R. and Y.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding.