Online Learning Resource Recommendation Approach Based on User Influence Perception
To address the issues of data sparsity in online learning and diverse resource demands,researchers have developed a hybrid recommendation strategy for online learning. This study introduces an online learning user model called LIAM,which is based on user similarity,knowledge credibility,and user influence assessment to enhance recommendation effectiveness. Additionally,the LIAM model is optimized by using a dynamic intuition fuzzy ( DIF ) strategy to improve recommendation accuracy and interpretability. Finally,a self-organizing recommendation method,SOR,is proposed to address the diversity and coverage of recommendation results,forming a comprehensive hybrid recommendation strategy for online learning. By using the Coursera dataset for performance validation,experimental results show that the SOR method outperforms two other representative recommendation methods. The SOR recommendation method is expected to provide more accurate and personalized recommendation services for online learning recommendation systems,thus enhancing learning outcomes and user experiences.
user influence perceptiononline learninghybrid recommendation
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