Design of personalized teaching resource recommendation system based on wireless communication network

The traditional teaching resource recommendation system uses features such as users' preference degree of resources to recommend corresponding resources according to the similarity degree. The limitations of content parsing and feature extraction lead to poor accuracy and low sparsity of the system recommended resources, which is difficult to meet the demand of personalized recommendation. To this end, we design a personalized teaching resource recommendation system based on wireless communication network. The wireless communication network of the system is built according to the actual system usage requirements of the campus. Design the hardware part of the system with S3C44B0 chip as the controller to collect students' usage data of resources and control the system operation. In the software part of the system, high frequency words of users' historical use of resources are extracted and resource recommendation paths are planned using Bayesian principle. After the collaborative filtering algorithm is processed by dimensionality reduction, personalized teaching resources recommendation is realized. The system function test results show that the average absolute error of the designed recommendation system is lower than that of the traditional system, and the sparsity of the system recommended resources is higher than 92%, which is more suitable for users' personalized needs and superior performance.