Online Vocational Education Personalized Course Content Push Algorithm Based on Big Data

A collaborative filtering based online teaching recommendation system for higher vocational colleges is proposed. UserCF system for user oriented personalized recommendation and object-oriented personalized recommendation system are established. The concept of multiprocessing is proposed to improve the learning performance of the network. By means of subject modeling, subject dictionary modeling, data preprocessing and behavior dictionary modeling, the dynamic information ontology is extracted. The dynamic ontology of network behavior is weighted by six factors, namely time, content, object, place, behavior, and number of reentries. This results in the decay model of user data in the network. Through the scoring matrix of user data, the degree of similarity between users and the filtering of data is to achieve the best recommendation. And the filtered data has less similarity and better identification, as well as less push delay. Practice has proved that this improved scheme is effective.