Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity

Abstract

The optimized learner evaluation matrix and similarity model are essential methods to deal with the challenges of “data sparsity” and “cold start” in the process of learning resource recommendation on the online learning platform. Accordingly, an improved collaborative filtering algorithm (TRCP) is proposed to improve the accuracy of learning resource recommendation. The TRCP algorithm generates the learner evaluation matrix for recommended projects by classifying learning resources. It comprehensively considers the influence of learners’ online learning behavior, learning time, and popularity of learning resources on learners’ interest and optimizes the sample data in the evaluation matrix. The experimental results on the school online teaching platform verified that this method has achieved obvious and effective results in both the accuracy and satisfaction rate of learning resource recommendation.

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

“…The result shows that the extracted key words are consistent with those generated by users and well summarize the contents of videos. Han [26] proposes an improved collaborative filtering algorithm (TRCP) to increase the accuracy of learning resource recommendation. TRCP algorithm generates the learner evaluation matrix of the recommended items by classifying the learning resources.…”

Retracted: Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”