## 1 Introduction  
The rapid transformation of the job market, fueled by technological evolution and globalization, has posed new challenges to recruiters and job seekers alike [1]. Traditional recruitment processes, often reliant on manual keyword searches or rule-based matching systems, struggle to cope with the increasing volume and complexity of job applications [2, 3]. In response, artificial intelligence (AI)-driven systems are being integrated into recruitment pipelines to improve the precision and scalability of candidate matching [4, 5, 6].  
A promising approach in this domain involves the use of deep-learning models for semantic matching between job descriptions and candidate resumes. Among such architectures, Siamese Neural Networks (SNNs) have demonstrated significant potential by learning a shared embedding space in which semantically similar job-candidate pairs are placed closer together [7; 8; 9; 10; 11; 12]. Although previous studies--including our earlier work on Zero-Shot Recommendation models [13]--have shown that pretrained sentence embeddings (e.g., Sentence-BERT or MiniLM) can yield impressive results in job-candidate alignment, such models often treat each document as a static unit and may not fully capture sequential or hierarchical structures commonly found in resumes and job postings.  
To address this limitation, this study explores the integration of sequential neural architectures--namely, Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) networks, and Transformers--within a Siamese network framework. These sequential \"head\" models are applied on top of pretrained sentence embeddings to capture the temporal or structural dependencies present in resumes and job descriptions. By doing so, we aim to leverage both contextual richness from pretrained models and the sequence modeling capabilities of GRU, LSTM, and Transformer architectures.  
The central goal of this paper is to evaluate and compare the effectiveness of these three sequential architectures as head modules within a Siamese network trained using triplet loss function. We focus on real-world IT recruitment data, consisting of multilingual resumes and job descriptions, and assess model performance using ranking metrics such as Top-K Accuracy and Mean Reciprocal Rank (MRR). Additionally, we provide qualitative analysis through t-SNE visualizations of the learned embedding spaces.  
Our contributions can be summarized as follows:  
* We introduce a modular Siamese framework that combines multilingual pretrained sentence embeddings with GRU, LSTM, and Transformer-based sequential heads for enhanced job-candidate matching.
* We conduct a systematic empirical comparison of these architectures using a real-world dataset derived from a commercial IT recruitment pipeline.
* We evaluate the models using both quantitative (ranking metrics) and qualitative (embedding space visualization) methods, demonstrating that the Transformer head yields the best overall performance.
* We analyze the practical implications of architectural choices in terms of matching precision, training dynamics, and computational tradeoffs.  
This study builds on our previous zero-shot recommendation model [13], expanding it through the addition of supervised training and advanced neural modeling strategies. The findings contribute to the development of more intelligent and scalable AI systems for modern recruitment workflows.