Online Educational Resources Recommendation Algorithm for English Translation Course Based on Collaborative Filtering

In order to recommend more teaching resources to students per unit of time, so that English translation courses can achieve better educational results, a collaborative filtering based online education resource recommendation algorithm for English translation courses is designed. The collaborative similarity is calculated to classify the behavioral preference data of educational resources. Based on this, the execution process of collaborative filtering algorithm is improved. This paper calculates the recommendation preference propagation parameters of educational resources, defines the target training conditions, solves the target recommendation function, and completes the design of online education resource recommendation algorithm for English translation courses. The experimental results show that under the action of the above recommendation algorithms, the maximum total amount of teaching resources obtained by student terminals in unit time has reached 6.7 Gb. Compared with hybrid recommendation algorithms and heterogeneous network recommendation algorithms, better educational results can be achieved in English translation courses.