Collaborative Filtering Recommendation of Music MOOC Resources Based on Spark Architecture

Wang, Lifu

doi:10.1155/2022/2117081

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…The user-based collaborative filtering algorithm calculates the similarity between users in the system and predicts the project according to the similar patterns between them [17][18][19]. This method is recommended by the "user-project score matrix" [20].…”

Section: A Brief Introduction To Collaborative Filtering Algorithmsmentioning

confidence: 99%

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

Han

2022

Advances in Multimedia

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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.

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Section: A Brief Introduction To Collaborative Filtering Algorithmsmentioning

confidence: 99%

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

Han

2022

Advances in Multimedia

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“…Initial attempts to offer additional relevant representation for retrieving included the use of spectral envelopes to identify timbral similarities [24,25] and attempts to record the beat of a song utilizing periodicity histograms [26] or temporal sequences [27]. However, the chosen feature sets' simplicity-which may still be classified as low-level-limited their popularity.…”

Section: High-level Resemblancementioning

confidence: 99%

Parametrized Optimization Based on an Investigation of Musical Similarities Using SPARK and Hadoop

Chaudhary,

Karthik,

Shankar

et al. 2023

SN COMPUT. SCI.

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The big data processing framework Spark is used to power a parameterizable recommender system that can make recommendations for music based on a user’s individual tastes and take into account a variety of musical tonal qualities. The system as it is presently built is completely scalable, which means that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio information, in addition to more cutting-edge similarity evaluations, may be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recommendations for a dataset including more than 114,000 songs may be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and similarity assessments. After the features have been retrieved, they are sent to the Spark-based recommender system to be processed. The calculated distance was displayed, examined, and graphically depicted. By computing the distance depending on the melody, rhythmic, and timbral components of the music, the final software controls song suggestion.

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“…Neural networks possess a high degree of nonlinearity, and convolutional neural networks are no exception to this rule, and the activation function is a part of the process that produces this important effect [14]. The commonly used activation functions are linear rectifier function, hyperbolic tangent activation function, and sigmoid() function, while convolutional neural networks usually use linear rectifier function as activation function.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

Design and Application of Online Courses under the Threshold of Smart Innovation Education

Wang¹,

Xiong²,

Zhu³

2023

IJACSA

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With the rapid development of the Internet and the growing demand for education, a new online teaching mode, massive open online courses (MOOC), emerged in 2012. To address the problems of sparse data and poor recommendation effect in online course recommendation, this paper introduces deep learning into course recommendation and proposes an auxiliary information-based neural network model (IUNeu), on the basis of which a collaborative neural network filtering model (FIUNeu) is obtained by improving it. Firstly, the principles and technical details of the deep learning base model are studied in depth to provide technical support for course recommendation models and online learning recommendation systems. In this paper, based on the existing neural matrix decomposition model (NeuMF), we combine user information and course information and consider the interaction relationship between them to improve the accuracy of the model to represent users and courses. The neural network model of auxiliary information (IUNeu) is incorporated into the online learning platform, and the system development is completed with the design of front and back-end separation, realizing the functions of the online learning module, course collection module, course recommendation module, and resource download module. Finally, the experimental results are analyzed: under the same experimental conditions, the test experiments are repeated 10 times, and the RMSE calculation results are averaged. The RMSE value of the neural network collaborative filtering model (FIUNeu) proposed in this paper based on deep learning is 0.85517, which is the best performance and has a high accuracy rate of rating prediction, and is useful for alleviating the data sparsity problem.

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Collaborative Filtering Recommendation of Music MOOC Resources Based on Spark Architecture

Cited by 6 publications

References 28 publications

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

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

Parametrized Optimization Based on an Investigation of Musical Similarities Using SPARK and Hadoop

Design and Application of Online Courses under the Threshold of Smart Innovation Education

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