Research and Implementation of Personalized Smart Teaching Platform Based on Collaborative Filtering Algorithm

Fast expansion of distance education in recent times availed English language learners with a considerable multitude of dynamic resources. But locating the required information from the vast ocean of available resources still remains a significant challenge for the users. An inventive solution to this problem is suggested in the paper making use of an advanced vector space representation along with the k-Means and collaborative filtering techniques for its cluster analysis. The algorithms are able to deftly classify and recommend diverse resources by carefully analyzing user behavior along with individual preferences, thus significantly enhancing the quality of the personalized service. This study concentrates on the possibilities offered by the application of such sophisticated algorithms in English education content retrieval systems, in a bid to improve resource allocation processes and learning pathways, hence achieving both better learning efficiency and user satisfaction.