AI-powered Resume-Job matching: A document ranking approach using deep neural networks

This study focuses on the importance of well-designed online matching systems for job seekers and employers. We treat resumes and job descriptions as documents. Then, calculate their similarity to determine the suitability of applicants, and rank a set of resumes based on their similarity to a specific job description. We employ Siamese Neural Networks, comprised of identical sub-network components, to evaluate the semantic similarity between documents. Our novel architecture integrates various neural network architectures, where each sub-network incorporates multiple layers such as CNN, LSTM and attention layers to capture sequential, local and global patterns within the data. The LSTM and CNN components are applied concurrently and merged together. The resulting output is then fed into a multi-head attention layer. These layers extract features and capture document representations. The extracted features are then combined to form a unified representation of the document. We leverage pre-trained language models to obtain embeddings for each document, which serve as a lower-dimensional representation of our input data. The model is trained on a private dataset of 268,549 real resumes and 4,198 job descriptions from twelve industry sectors, resulting in a ranked list of matched resumes. We performed a comparative analysis involving our model, Siamese CNN (S-CNNs), Siamese LSTM with Manhattan distance, and a BERT-based sentence transformer model. By combining the power of language models and the novel Siamese architecture, this approach leverages both strengths to improve document ranking accuracy and enhance the matching process between job descriptions and resumes. Our experimental results demonstrate that our model outperforms other models in terms of performance.