Design and Development of a Skill Gap Analyzer for Job Recommendation Using Deep Learning

Abstract

The high pace of technological advancement and changes in the industry demand has created a massive discrepancy between the skills available to job seekers and those required by the employers. The traditional job portals mainly use the matching based on keywords and as such, the matching process fails to reflect on the contextual meaning and semantic relationship between the resume and job description. To overcome this, the design and development of a Skill Gap Analyzer based on Deep Learning to perform intelligent job recommendation will be presented in this paper. The suggested system employs the latest methods of the Natural Language Prc ocessing (NLP) to automatically derive structured skills out of resumes and job descriptions. Transformer-based embeddings are used to draw semantic similarity and a hybrid similarity framework increases the matching accuracy. The system recognizes the lack of competencies and provides individual employment suggestions and suitable learning recommendations to enhance employability. Standard performance measures such as Precision, Recall, F1 score, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG) were used as the performance measures to evaluate the experimental results. Findings reveal that the performance based on classification and ranking has greatly improved when compared to traditional methods of using keywords. On the whole, the suggested framework can be useful in closing the gap between education and employment through offering precise, context-sensitive, and individualized career guidance solutions.

Introduction

Across all the provided texts, the core idea is the shift from traditional, manual, or rule-based systems toward intelligent, data-driven solutions using AI, machine learning, and modern computing frameworks to solve real-world problems in infrastructure, communication systems, healthcare materials, environmental monitoring, governance, and workforce management.

In engineering and infrastructure domains, Structural Health Monitoring (SHM) has evolved from manual inspection to sensor-based, vibration-driven, and AI-assisted systems. These systems use real-time sensor data and advanced signal processing to detect damage in bridges and buildings, enabling early warning, improved safety, and better maintenance planning. However, challenges remain in standardization, sensor placement, and handling environmental noise.

In communication and distributed computing, Federated Learning (FL) addresses privacy limitations of centralized machine learning by training models across decentralized devices without sharing raw data. While widely applied in mobile, healthcare, and finance, FL still faces issues like data heterogeneity, communication cost, security risks, and system imbalance, requiring improved optimization and robust aggregation methods.

For attendance systems, traditional manual and even early electronic methods are being replaced by RFID and IoT-based solutions integrated with cloud platforms. These modern systems improve accuracy, reduce proxy attendance, enable real-time tracking, and support large-scale deployment. However, biometric and IoT systems introduce challenges such as cost, privacy concerns, and dependency on internet connectivity.

In healthcare and pharmaceutical research, the medicinal mushroom Ganoderma lucidum (Reishi/Lingzhi) is highlighted for its bioactive compounds like triterpenoids and polysaccharides, which provide anti-inflammatory and immunomodulatory effects. Despite its long traditional use, variability in cultivation and product quality necessitates standardized chemical analysis and quality control.

In environmental science, air quality forecasting—particularly for cities like Pune—is increasingly driven by machine learning models such as Facebook Prophet. These models help predict AQI trends by capturing seasonality and long-term patterns. Results show clear seasonal pollution cycles influenced by weather, though limitations remain in handling sudden events and external factors.

In voting systems, blockchain-based approaches are being explored to overcome weaknesses of traditional paper ballots and centralized electronic voting. Blockchain improves transparency and tamper resistance through immutable ledgers, but challenges include scalability, usability, and system complexity. Hybrid models combining web interfaces with blockchain storage offer a practical compromise.

Finally, in workforce and recruitment systems, the skill gap problem is addressed using AI-driven job recommendation systems. Traditional keyword-based methods are insufficient, so modern approaches use NLP, embeddings, and deep learning (CNN, RNN, LSTM, BERT) to analyze resumes and job descriptions. These systems identify missing skills, recommend jobs, and suggest learning paths, achieving higher accuracy and personalization compared to conventional methods.

Conclusion

Deep learning has taken the form of disruptive solution in developing smart skill gap analysis systems. With established Natural Language Processing (NLP) and transformer-based models, the current systems can no longer rely on naive methods of matching keywords to find relevant information on resumes and job descriptions, but rather gain a comprehensive insight into the answer. This facilitates better determination of the competencies that are missing, contextual competency links and opportunity jobs. As this paper has shown, deep learning models are vastly superior to traditional ones in the aspects of accuracy, precision, recall and ranking capability, and thus can give more useful and valuable recommendations. Automated skill extraction and hybrid similarity computation is one of the most important contributions of deep learning in analyzing the gap in skills. By using these methods, systems are able to effectively process unstructured text information, and create custom learning streams that are specific to the personal career objectives. As a result, these systems do not only enhance the accuracy of job recommendations, but they also enhance employability through focusing the user towards skill development. Although there are these advantages, there are some challenges. Problems with data quality and standardization can influence the performance of the model, whereas the problem of algorithmic bias can impact the fairness of the recommendations. Also, deep learning models tend to be non-transparent, which is a cause of privacy in explainability and trust to the user. These are critical issues to be addressed in order to implement them on a large scale. Future studies are required to consider the combination of explainable AI, real-time labor market analytics, and adaptive learning systems that constantly change recommendations to meet the changing needs of industries. In general, smart skill gap analyzers have a tremendous potential to close the disjuncture between education and job, maximize labor use, and contribute to the sustainable careers growth in the fast digital economy.

References

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Copyright

Copyright © 2026 Aqeeb Mohammed Ismail Khan, Mrs. Veena Kulkarni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.