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

Author:

Han Xiaolu1ORCID

Affiliation:

1. Department of Finance and Business, Anhui Vocational and Technical College, Hefei 230051, China

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.

Funder

Quality Engineering Project of Anhui Colleges and Universities

Publisher

Hindawi Limited

Subject

General Computer Science

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