Research and Application of Online Course Recommendation System Based on TF-IDF Algorithm

Abstract

In recent years, with the rapid development of Internet technology, a large number of online learning resources have emerged. Especially affected by the COVID-19 epidemic, online learning has become a very effective learning means. However, a large number of learning platforms and massive online teaching resources have the following three problems: 1) The quality of these courses is uneven and the evaluation standards are different; 2) There are so many similar courses that it is difficult for learners to distinguish them; 3) These classes are lack of unity and integration, and it is hard to recommend any hierarchical, coherent and systematic course resources to learners. Therefore, a recommendation model based on TF-IDF algorithm is designed to extract personalized-featured courses, use the nearest neighbor similarity to cluster the similarity of similar courses, and conduct the featured portrait of learners to realize online courses recommendation. Combined with the model design, this paper presents a tag-based online course resource recommendation system, which can fully explore learners' explicit and implicit preferences according to course tags, and recommend satisfactory MOOC resources for them with good application value.