An Automatic Recommendation Method for English Language Education Resources Based on Particle Swarm Algorithm

This study addresses the challenge of maintaining an up-to-date and diverse university English resource library by proposing an advanced method that leverages deep learning and particle swarm optimization (PSO). Existing systems often struggle to efficiently integrate new, varied educational materials while balancing user satisfaction with educational effectiveness. To bridge this gap, we introduce a PSO-based multi-objective recommendation framework that incrementally updates the resource library, ensuring it remains both relevant and comprehensive. Our method begins with automated data collection from sources such as academic papers, news, and educational materials, followed by rigorous data cleaning and standardization using natural language processing (NLP) techniques. At the core of our system is a fine-tuned GPT-3 model, optimized to identify and categorize resources accurately. The multi-objective PSO algorithm then balances user satisfaction with educational value, meeting both immediate learning needs and long-term goals. Continuous user feedback further refines our approach, keeping recommendations aligned with evolving educational demands. Experimental results demonstrate that our approach surpasses existing methods in precision, coverage, diversity, and novelty, establishing it as a robust solution for AI-driven educational resource management.