Open AccessResearch on Recommendation of Online Materials Course Resources Based on Text Similarity
Author(s) -
Mengying Yao Ziyu Liu
Publication year - 2021
Publication title -
converter
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.104
H-Index - 1
ISSN - 0010-8189
DOI - 10.17762/converter.161
Subject(s) - similarity (geometry) , computer science , cosine similarity , information retrieval , ranking (information retrieval) , preprocessor , similarity learning , course (navigation) , data pre processing , world wide web , data mining , artificial intelligence , pairwise comparison , pattern recognition (psychology) , engineering , image (mathematics) , aerospace engineering
In order to solve the problem that it is difficult for college students to find learning resources related to thecourses they are learning quickly and accurately in blended learning. This paper proposes an online materials course resources recommendation method based on text similarity. Firstly, collecting the data of course resources on the online learning platform through web crawler technology. Secondly, preprocessing the data which contend deleting noise data, the Chinese word segmentation and calculating the course similarity based on cosine similarity then getting the course recommendation results according to the similarity ranking. Thirdly, evaluating the recommendation results and optimizing the similarity calculation method according to the evaluation results. Finally, the learners are recommended curriculum resources according to the similarity ranking results. According to the courses learned on the Superstar platform, the experiment recommends similar course resources on the XueYin Online platform. The results show that the online materials course resources recommendation method based on text similarity can recommend relevant online materials course resources for learners quickly and accurately, which has certain reference significance and application value for online course resources recommendation.