Job-Profile Matching with CTN and MADRL with GEABB: A Recommender System

In this paper, we propose a novel approach to address the complex and challenging task of job-profile matching by enhancing the performance of the co-teaching network model, which is a state-of-the-art deep learning-based approach. Our approach involves implementing a self-adaptable algorithm that integrates an optimisation layer and adaptive building blocks. The proposed algorithm is trained and tested on a Kaggle data-set, and our numerical experiment demonstrates that the integration layer improved the accuracy of the co-teaching network by 5%, significantly enhancing the performance of the model in job-profile matching with real-world data. Our approach also incorporates self-adaptive building blocks that can adapt to changing environments and improve prediction accuracy, and the integration of multi-agent deep reinforcement learning, particle swarm optimisation, and grammatical evolution enhances the optimisation process and improves the accuracy of the model. Overall, our study presents a novel integration of adaptive building blocks and multi-agent deep reinforcement learning with the co-teaching network for job-profile matching and shows that our approach can significantly improve the performance of existing state-of-the-art models in this area.