Customized Biotechnology Learning Resource Recommendations: Enhancing English Education through Collaborative Filtering Technology

Abstract

The conventional methods for recommending English teaching resources in biotechnology education often yield suboptimal results, failing to meet the nuanced retrieval needs of users deeply engaged in biotechnological studies. To address this, we have integrated collaborative filtering technology to refine and enhance the personalized recommendation of English teaching resources. Specifically tailored for biotechnology learning. A comprehensive collection of English teaching resources pertinent to biotechnology was amassed initially. These resources underwent meticulous data extraction and classification to produce a refined, processed dataset, serving as the backbone for our resource database. Leveraging the collaborative filtering method, we analyzed the congruence between user preferences — primarily from students and professionals in the biotechnology sector — and the available biotechnology-focused English video teaching resources. This analysis facilitated the generation of a resource-matching recommendation list meticulously curated to support the unique learning trajectories in biotechnology. The culmination of this process is a bespoke recommendation system for English teaching resources, fine-tuned with collaborative filtering algorithms and specifically designed for the biotechnology education sector. Our experimental results are promising, showcasing a recommendation accuracy rate of 92% and the ability to deliver the fastest resource recommendations within 1.4 seconds. Our method, which intricately blends collaborative filtering algorithms with a deep understanding of biotechnological learning needs, holds substantial practical value. It enhances the efficacy of English education in biotechnology and aligns with the evolving educational demands of this dynamic field.