Research on Recommendation of Online Materials Course Resources Based on Text Similarity

Abstract

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.