The effectiveness of education heavily depends on the alignment between student needs and the teaching style of educators. In this paper, we present a teacher recommendation system utilizing the k-Nearest Neighbors (KNN) algorithm, implemented with CUDA (Compute Unified Device Architecture) for accelerated performance on GPU hardware. The recommendation system aims to assist educational institutions in matching teachers to students or courses based on various attributes such as expertise, student feedback, and course requirements. Traditional KNN algorithms, while effective, are computationally expensive when dealing with large datasets, which is a common challenge in modern educational systems. By leveraging the parallel processing capabilities of CUDA on GPUs, our implementation significantly reduces the time complexity of the KNN algorithm, enabling real-time recommendations even with extensive datasets. We evaluate the system on multiple datasets, demonstrating improved accuracy and efficiency. The experimental results reveal that GPU-accelerated KNN provides a scalable solution that outperforms conventional CPU-based implementations, offering a practical approach for large-scale teacher recommendation systems.
CONTRIBUTION: @Rahul @Ranjith @Sai Mahesh