English teaching resource recommendation based on IoT location context localization algorithm

To improve the efficiency of English teaching resource utilization, this study proposes an intelligent resource recommendation technology based on the Internet of Things. Specifically, the sparrow algorithm is used to locate node information, and dynamic step size and simulated annealing algorithms are introduced to optimize the model. Building on these methods, this study further combines clustering and collaborative filtering algorithms to construct a recommendation model that achieves personalized recommendations of English teaching resources. In the node positioning experiment, the research model had excellent positioning accuracy. For example, when the ranging error was 10% and 30%, its average position error was 5.26% and 10.2%, showing the best performance. At the same time, in tests with different anchor node ratios, the positioning accuracy of the research model remained the best, with a minimum positioning error of 2.2 m. In the resource recommendation experiment, compared to similar techniques, the research model performed the best in recall and accuracy. In terms of the recommendation effect of English dialogue resources, the research model had an average accuracy of 93.8%, and the overall performance was the best. In the recommended review resources, the training error of the research model was the lowest, at 0.355. Overall, the research model has excellent application effects. The research content will provide technical references for the digital construction of education and English resource management.