A Model for Learning Language with a Collaborative Filtering equipped Recommender System
Jinbo Li
Journal of Information & Systems Management, Vol. 15, No. 4, 2025.
DOI: https://doi.org/10.6025/jism/2025/15/4/169-176
Link: https://www.dline.info/jism/fulltext/v15n4/jismv15n4_2.pdf
Abstract:
The paper explores the development of an English teaching resource library enhanced by a personalized
information recommendation model based on the collaborative filtering algorithm. It highlights current
challenges in China's higher education context, such as low adoption rates of digital English resources, insufficient
standardization, poor inter institutional communication, and static, disorganized content that fails
to meet learner's diverse needs. The author argues that integrating big data and intelligent algorithms can
transform traditional, static resource libraries into dynamic, interactive platforms. Collaborative filtering is
proposed as a solution to overcome issues like data sparsity and imprecise recommendations by analyzing
user behavior such as click frequency and browsing history to predict preferences and deliver tailored
English learning materials. The paper outlines a two layer recommendation system structure: a user interaction
layer and a recommendation response layer. Experimental results from a sample of 2,000 university
students show that those using the recommendation enhanced resource library demonstrated measurable
improvements in English proficiency, whereas the control group did not. The study concludes that the
collaborative filtering algorithm significantly boosts recommendation accuracy and learning outcomes,
supporting more efficient, personalized, and engaging English education in digital environments. This approach
not only optimizes resource utilization but also aligns teaching content with individual learner needs,
marking a meaningful advancement in intelligent educational platforms.