## 4 Conclusions  
In this paper, we presented a Siamese network framework enhanced with sequential heads (GRU, LSTM, and Transformer) for the purpose of matching job descriptions and candidate resumes in an IT recruitment context. Leveraging a multilingual pretrained Sentence Transformer base, our architecture effectively encoded rich semantic representations from job and resume texts, while the sequential heads captured longer-range dependencies and contextual nuances. Through a detailed evaluation using real-world recruitment data, we observed that all three proposed head models achieved competitive performance in ranking and retrieval metrics, with the Transformer-based approach providing slightly better results in terms of Top-K accuracy and Mean Reciprocal Rank (MRR) results. Our findings align with the broader NLP literature, confirming that self-attention mechanisms are particularly well suited for handling complex textual relationships and longer documents. Nonetheless, the GRU and LSTM heads also provided strong outcomes, suggesting that the choice of architecture may depend on practical constraints such as available computational resources or the specific ranking goals of a given recruitment system. In particular, we found that the GRU was highly efficient and converged rapidly, while the LSTM proved useful for tasks requiring more explicit long-term memory structures. The proposed models demonstrate considerable promise in reducing time-to-hire and enhancing overall recruitment accuracy by automatically surfacing candidates most aligned with a role's requirements. The clear separation in the learned embedding space between positive and negative examples indicates robust discriminative capabilities, thus paving the way for improved job-candidate matching in production-scale settings. Future WorkSeveral avenues for future research and practical enhancements of the presented approach can be identified:  
* Integration of Structured Data: While our approach focused on unstructured text (job and resume content), incorporating tabular metadata (e.g., candidate location, years of experience, and skill taxonomies) might further refine matching outcomes. * Domain Expansion: Although our dataset was derived from IT-specific recruitment pipelines, extending experiments to other industry sectors or roles with distinct terminologies (e.g., finance, marketing, healthcare, etc.) would test model generalization capabilities and highlight potential domain adaptation strategies. * Explainability and Interpretability: Providing interpretability (e.g., attention heatmaps or saliency scores) would offer transparency to recruiters, helping them understand why certain candidates rank higher than others and thus increasing the trust in the system's recommendations. * Real-Time Inference Optimization: Transformer heads can be computationally demanding in real-time ranking scenarios. Investigating model distillation, quantization, or efficient Transformers could make the approach more practical for large-scale deployments. * Active Learning and Online Updates: In real recruitment environments, data distribution shifts occur frequently (e.g., new technologies, changing skill demands, etc.). Implementing active learning, continuous fine-tuning, or online learning methods can help the models to remain current and effective. By pursuing these directions, we aim to deliver more comprehensive, transparent, and scalable solutions for job-candidate matching, ensuring that advanced NLP technologies continue to benefit both hiring organizations and job seekers alike. **Author Contributions:** Conceptualization, M.L., J.K. and T.L. ; methodology, J.K, M.L. and T.L. ; software, M.L., J.K., B.A. and T.L. ; validation, M.B., B.S., J.K. and M.L. ; formal analysis, M.L. ; investigation, B.S., T.L. and B.A. ; resources, L.D. and R.Z. ; data curation, G.B. and B.N. ; writing--original draft preparation, J.K. and I.A. ; writing--review and editing. J.K. and I.A. ; visualization, M.B. and B.A. ; supervision, L.D. and J.K.; project administration, L.D.