Design of Mobile English Teaching Platform Based on Collaborative Filtering Algorithm.

Abstract

With the rapid development of information technology, today's talent training mode is no longer limited to traditional school education. At the same time, with the maturity of portable mobile devices, a new learning method—mobile learning has been born. In this paper, the narrative of data is taken into account in the way of user collaborative filtering recommendation. For prefilling the matrix, the project confidence level also needs to be considered during the whole process. The project confidence level is measured by information entropy model. In the process of correction, it combines with traditional cosine similarity, calculates the user similarity matrix, can budget equalization, and expands the original matrix. After filling the matrix, the user uses the method of similarity calculation, using Pearson similarity and combining with the Euclidean distance correction method. When comparing the particular result prediction with the actual prediction following the completion of the similarity matrix data, all results point to a considerable reduction in MAE and RMSE. The user does not assess the item score to forecast. This demonstrates how this technique may enhance the reliability and consistency of the mobile English system platform.