University English Network Teaching Resource Bank Construction Based on Cooperative Filter Algorithm

Abstract

In recent years, with the rapid development of the network, related applications have also been greatly developed. Online-based online learning systems allow learners to ignore the difference in time and more convenient learning. It does not stick to the characteristics of classroom learning, which greatly meets the characteristics of contemporary education, and also enriches the diversity of education. This paper optimizes the recommendation technology of the personalized recommendation system of college English network teaching resource library, which is to propose a recommended model for the synergistic filtering algorithm using a combination of project properties and user attributes. Based on the detailed analysis of the network teaching resource construction platform, the algorithm is applied to the network teaching system, filled into the original matrix to obtain a dense pseudocomment matrix, and then find the user’s own attribute information and rating information in the pseudocommentary matrix. The recent neighbor of the target user calculates the target user’s preevaluation project for the nonscore item based on the nearest neighboring rating information and finally receives the highest recommendation list. After experimental analysis, the collaborative filtering algorithm proposed in this article was significantly higher than that of the remaining two, and the precision and recall values were also slightly more than about 0.2 in the $F$ -measure evaluation criteria, bounded about 2%.