Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity
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...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/2584890 |
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| _version_ | 1849468939942232064 |
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| author | Xiaolu Han |
| author_facet | Xiaolu Han |
| author_sort | Xiaolu Han |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ff02714f5eb64f2ca6ca9fc0f65cc508 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-ff02714f5eb64f2ca6ca9fc0f65cc5082025-08-20T03:25:41ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/2584890Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and PopularityXiaolu Han0Department of Finance and BusinessThe 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.http://dx.doi.org/10.1155/2022/2584890 |
| spellingShingle | Xiaolu Han Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity Advances in Multimedia |
| title | Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity |
| title_full | Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity |
| title_fullStr | Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity |
| title_full_unstemmed | Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity |
| title_short | Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity |
| title_sort | recommendation algorithm of web based learning resources considering timeliness and popularity |
| url | http://dx.doi.org/10.1155/2022/2584890 |
| work_keys_str_mv | AT xiaoluhan recommendationalgorithmofwebbasedlearningresourcesconsideringtimelinessandpopularity |