Teaching learning-based optimisation algorithm: a survey

Mishra, Ruchi; Sharma, Nirmala; Sharma, Harish

doi:10.1504/ijsi.2020.111164

Cited by 2 publications

(1 citation statement)

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“…From a data perspective, the recommendation system mitigates the negative impact of the Matthew effect, while enhancing the ability to tap into long-tail content. In the context of massive digital music, the traditional operation strategy makes the Matthew effect particularly obvious [ 11 ]. The long-lasting invariance of the popular song charts makes it difficult to discover new songs on the shelves and exacerbates the problem of the sparsity of user listening behaviour [ 12 ].…”

Section: Status Of Researchmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

2022

Computational Intelligence and Neuroscience

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In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user’s preferred features and the music potential features are calculated to generate recommendations for the target user. The user-music dataset for model training and testing is constructed in-house, and the network model structure used for system experiments is designed based on a typical convolutional neural network model, while the model training tuning parameters are compared and selected. Finally, the model is trained and tested in this paper, and the system is evaluated in terms of both prediction rating accuracy and recommendation list generation accuracy using root mean square error, accuracy, recall, and F1 value as recommendation quality evaluation metrics. The experimental results show that the recommendation algorithm in this paper has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, this paper makes full use of the powerful advantage of deep neural networks to automatically extract features and obtain higher-level music feature representations from the audio content, while incorporating the historical behavioural information of user interactions with music, which can effectively alleviate the problems such as cold start in recommendation systems.

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Section: Status Of Researchmentioning

confidence: 99%