发布时间：2025-04-30

论文名称：A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map

发表刊物：IEEE Access

摘要：Learning resource recommendation, such as curriculum video recommendation, is an effec- tive way to reduce cognitive overload in online learning. The existing curriculum video recommendation systems are generally limited to one course, ignoring the knowledge correlation between courses. In this work, we propose a two-stage cross-curriculum video recommendation algorithm that considers both the learners’ implicit feedback and the knowledge association between course videos. First, we use collaborative filtering to generate a video seed set, which is based on the learner’s implicit video feedback, such as video learning frequencies, video learning duration, and video pausing and dragging frequencies. Second, we construct a cross-curriculum video-associated knowledge map and use a random walk algorithm to measure the relevance of the course videos. The relevance is based on each video seed as a starting node and is extended to a video subgraph. Then, several cross-curricular video-oriented subgraphs are recommended for the learners. The experimental results indicate that our cross-curriculum video recommendation algorithm performs better than the traditional collaborative filtering-based recommendation algorithms in terms of accuracy, recall rate and knowledge relevance.

合写作者：朱海萍，刘雨，田锋（通讯作者），吴轲等

是否译文：否

发表时间：2018-09-19