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

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. © 2022 IEEE.

A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM plus Attention

With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.