An intelligent deep learning-enabled recommendation algorithm for teaching music students

Tang, Changfei; Zhang, Jun

doi:10.1007/s00500-021-06709-x

Cited by 17 publications

(7 citation statements)

References 15 publications

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“…Aiming at the problems existing in the current network education field, it is an important work in the field of intelligent education to study how to fully mine and explore the valuable data of the online education platform and find the relationship between learners and learning resources. On this basis, we accurately recommend the required courses for learners by using multi-source heterogeneous learning behavior data [16,17]. Reference [18] calculated the importance of external attribute tolerance and internal attribute quality value on the course and built the LDA user interest model on this basis to calculate the user's preference for the topic and realize the recommendation of personalized learning resources.…”

Section: Related Workmentioning

confidence: 99%

Personalized Course Recommendation Method Based on Learner Interest Mining in Educational Big Data Environment

Zhang

2022

Scientific Programming

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Aiming at the problems of low accuracy and large limitations of the current personalized course recommendation method in the educational big data environment, a personalized course recommendation method based on learner interest mining in the educational big data environment is proposed. First, a corresponding online course recommendation model framework is proposed by adopting GRU, which can effectively solve the problems of gradient disappearance and gradient explosion in the process of training the RNN neural network. Then, by introducing an auto-regressive language model, XLNet (Generalized Autoregressive Pretraining for Language Understanding), the information missing problem under the Mask mechanism in the BERT model is effectively optimized, and bidirectional prediction is achieved. Finally, by introducing a temporal attention mechanism into the model, enough attention is assigned to highlight local important information on key information, which improves the quality of hidden layer feature extraction, and a high-accuracy personalized course recommendation based on learner interest mining is realized. The proposed algorithm is compared with the other three collaborative filtering algorithms and the RNN algorithm through simulation experiments. The results show that the precision, recall, and F1-measure of the proposed algorithm in the personalized course recommendation results for different types of courses under the condition of the same database are all optimal. The largest values were 92.1%, 89.3%, and 90.7%, respectively. The overall performance is better than other comparison algorithms. This method can improve the accuracy and optimization limitations of personalized courses and can fully tap the interests of learners. It is of great significance for learners to choose personalized courses in the current educational big data environment.

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Section: Related Workmentioning

confidence: 99%

Personalized Course Recommendation Method Based on Learner Interest Mining in Educational Big Data Environment

Zhang

2022

Scientific Programming

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“…With superior natural language understanding, LLMs are able to understand user preferences, item descriptions, and contextual information to provide more satisfactory recommendations. In addition, deep learning has also performed very well in the field of recommendation systems [11][12][13]. This approach leverages the ability of neural networks to learn complex patterns from data, making it particularly powerful for recommendation tasks.…”

Section: Introductionmentioning

confidence: 99%

Stacked Noise Reduction Auto Encoder–OCEAN: A Novel Personalized Recommendation Model Enhanced

Wang,

Zheng,

Wang

et al. 2024

Systems

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With the continuous development of information technology and the rapid increase in new users of social networking sites, recommendation technology is becoming more and more important. After research, it was found that the behavior of users on social networking sites has a great correlation with their personalities. The five characteristics of the OCEAN personality model can cover all aspects of a user’s personality. In this research, a micro-directional propagation model based on the OCEAN personality model and a Stacked Denoising Auto Encoder (SDAE) was built through the application of deep learning to a collaborative filtering technique. Firstly, the dimension of the user and item feature matrices was lowered using SDAE in order to extract deeper information. The user OCEAN personality model matrix and the reduced user feature matrix were integrated to create a new user feature matrix. Finally, the multiple linear regression approach was used to predict user-unrated goods and generate recommendations. This approach allowed us to leverage the relationships between various factors to deliver personalized recommendations. This experiment evaluated the RMSE and MAE of the model. The evaluation results show that the stacked denoising auto encoder collaborative filtering algorithm can improve the accuracy of recommendations, and the user’s OCEAN personality model improves the accuracy of the model to a certain extent.

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“…Various approaches utilizing Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and self-attention mechanisms have been proposed to learn underlying behavioral representations of user preferences that can be used to characterize a user's sequential interactions with items [13] . In addition, researchers have integrated rich contextual information (e.g., 3D other features of interacting items) into neural sequential recommendation networks [14] . It has been shown that contextual information contributes significantly to the performance of sequential recommendation systems [15][16] .…”

Section: Introductionmentioning

confidence: 99%

Sequential music recommendation based on self-attention mechanism and content characteristics

Bai

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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Real music recommendation is a challenging problem, aiming at selecting items that users may be interested in from massive data. Recently, the recommendation algorithm based on deep learning has left a deep impression. However, most of the recommendation methods ignore the content features of music itself or simply splice it with other features, which to some extent affects the recommended system performance. This paper mainly introduces a music recommendation method C_Rec based on self-attention mechanism and convolution neural network. It uses a new method to fuse the features of music vocal music, which improves the scalability of the recommendation method. Experiments on the corresponding data sets show that our approach is progressiveness compared with some existing models.

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An intelligent deep learning-enabled recommendation algorithm for teaching music students

Cited by 17 publications

References 15 publications

Personalized Course Recommendation Method Based on Learner Interest Mining in Educational Big Data Environment

Personalized Course Recommendation Method Based on Learner Interest Mining in Educational Big Data Environment

Stacked Noise Reduction Auto Encoder–OCEAN: A Novel Personalized Recommendation Model Enhanced

Sequential music recommendation based on self-attention mechanism and content characteristics

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