Research on Recommendation System of Online Chinese Learning Resources Based on Multiple Collaborative Filtering Algorithms (RSOCLR)
Tianyu Wang, Dong Ge · International Journal of Human-Computer Interaction · 2023
This study is an innovative study on intelligent online language teaching that is based on the development of online Chinese learning resources and learner needs. The recommendation technology of collaborative filtering algorithm is used to find, analyze and recommend online Chinese learning resources that meet the needs of learners and teachers. This study assists learners and teachers in quickly searching for various learning resources under specified conditions, analyzing the resources obtained in detail, and providing results based on machine learning and big data sample analysis. The research is structured as follows to highlight the characteristics of Chinese learning resources and the personalized demand orientation of recommendation results: (1) Based on the personalized demand orientation of learners’ language learning and the goal oriented theoretical standards of Chinese teaching, a recommendation system based on multiple collaborative filtering hybrid algorithms is designed; (2) Evaluate the operation of the recommendation system using teaching practice; (3) This article investigates the influence of online language learning from technical factors and the operation mechanism of Chinese learning resource recommendation. The results of the experiments show that this hybrid approach has some advantages when it comes to recommending Chinese learning resources.HIGHLIGHTSA system for recommendating online Chinese learning resources is constructed.The recommendation results based on learners’ personalized needs and learning goal oriented design have high accuracy.The recommendation system based on hybrid mode has a high recommendation effect.The recommendation system can find and predict learners’ learning needs.Cultural differences, mental states, and learning psychology from evaluators may lead to differences in recommendation results.