Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm
This study proposes a teaching resource recommendation method (TRRDLMF) based on deep learning and probabilistic matrix decomposition, aiming to improve teaching resource recommendation’s accuracy and personalization level. The study combines hybrid neural network feature extraction of teaching reso...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0543 |
| _version_ | 1797279111730692096 |
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| author | Ma Junxia Liu Qilin Zhang Zhifeng Gu Peipei |
| author_facet | Ma Junxia Liu Qilin Zhang Zhifeng Gu Peipei |
| author_sort | Ma Junxia |
| collection | DOAJ |
| description | This study proposes a teaching resource recommendation method (TRRDLMF) based on deep learning and probabilistic matrix decomposition, aiming to improve teaching resource recommendation’s accuracy and personalization level. The study combines hybrid neural network feature extraction of teaching resources and user feature extraction based on extended noise-reducing self-encoder and extended probabilistic matrix decomposition-based teaching resource recommendation technique. The study validates the model’s effectiveness by conducting empirical analyses on four online learning platform datasets of different sizes. The recommendation system can effectively track learners’ knowledge mastery status and learning preferences, and provide corresponding teaching resources. Test question similarity and difficulty analysis results show that the model can accurately capture the correlation between test questions and provide complementary data in education. The personalized recommendation analysis reveals the learners’ knowledge preference states that change with learning, demonstrating the model’s ability to give explanations at the level of learners’ knowledge preferences. The analysis of the impact of data size on recommendation results shows that the recommender system can achieve up to 73.12% accuracy and 97.89% recall for different TOP-K recommendation lists, with the best F-value at TOP-6. The personalized recommendation system for teaching resources based on the joint probability matrix decomposition model and CNN improvement algorithm proposed by the research demonstrates significant effects in improving the accuracy and personalization level of teaching resources recommendation, which helps learners to acquire suitable learning resources more efficiently, and has high practical value and promotion potential. |
| first_indexed | 2024-03-07T16:20:34Z |
| format | Article |
| id | doaj.art-39325e6e591246338369ee68481ecd5f |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T16:20:34Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-39325e6e591246338369ee68481ecd5f2024-03-04T07:30:42ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0543Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithmMa Junxia0Liu Qilin1Zhang Zhifeng2Gu Peipei31College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450000, China.1College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450000, China.1College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450000, China.1College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450000, China.This study proposes a teaching resource recommendation method (TRRDLMF) based on deep learning and probabilistic matrix decomposition, aiming to improve teaching resource recommendation’s accuracy and personalization level. The study combines hybrid neural network feature extraction of teaching resources and user feature extraction based on extended noise-reducing self-encoder and extended probabilistic matrix decomposition-based teaching resource recommendation technique. The study validates the model’s effectiveness by conducting empirical analyses on four online learning platform datasets of different sizes. The recommendation system can effectively track learners’ knowledge mastery status and learning preferences, and provide corresponding teaching resources. Test question similarity and difficulty analysis results show that the model can accurately capture the correlation between test questions and provide complementary data in education. The personalized recommendation analysis reveals the learners’ knowledge preference states that change with learning, demonstrating the model’s ability to give explanations at the level of learners’ knowledge preferences. The analysis of the impact of data size on recommendation results shows that the recommender system can achieve up to 73.12% accuracy and 97.89% recall for different TOP-K recommendation lists, with the best F-value at TOP-6. The personalized recommendation system for teaching resources based on the joint probability matrix decomposition model and CNN improvement algorithm proposed by the research demonstrates significant effects in improving the accuracy and personalization level of teaching resources recommendation, which helps learners to acquire suitable learning resources more efficiently, and has high practical value and promotion potential.https://doi.org/10.2478/amns-2024-0543deep learningprobability matrix decompositionfeature extractionsimilarityteaching resources recommendation97m50 |
| spellingShingle | Ma Junxia Liu Qilin Zhang Zhifeng Gu Peipei Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm Applied Mathematics and Nonlinear Sciences deep learning probability matrix decomposition feature extraction similarity teaching resources recommendation 97m50 |
| title | Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm |
| title_full | Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm |
| title_fullStr | Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm |
| title_full_unstemmed | Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm |
| title_short | Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm |
| title_sort | research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and cnn improvement algorithm |
| topic | deep learning probability matrix decomposition feature extraction similarity teaching resources recommendation 97m50 |
| url | https://doi.org/10.2478/amns-2024-0543 |
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