<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper proposes a simulation algorithm of transition probability function based on logistic distribution. This method mainly models popularity and state transition probability functions by acquiring consumers’ music preferences and likes. Through this mathematical model, this paper obtains the best results that are more in line with consumer preference. This paper conducts a simulation experiment by collecting Netease cloud music data. Finally, through the comparison with the empirical data, it is further demonstrated that the algorithm model in this paper has particular practical value.</p>
</abstract>

This paper proposes a simulation algorithm of transition probability function based on logistic distribution. This method mainly models popularity and state transition probability functions by acquiring consumers’ music preferences and likes. Through this mathematical model, this paper obtains the best results that are more in line with consumer preference. This paper conducts a simulation experiment by collecting Netease cloud music data. Finally, through the comparison with the empirical data, it is further demonstrated that the algorithm model in this paper has particular practical value.

Music Recommendation Index Evaluation Based on Logistic Distribution Fitting Transition Probability Function

Henan University of Engineering Zhixing College

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper proposes a simulation algorithm of transition probability function based on logistic distribution. This method mainly models popularity and state...

Music Recommendation Index Evaluation Based on Logistic Distribution Fitting Transition Probability Function

Data publikacji: 15 lip 2022

Zakres stron: 1769 - 1776

Otrzymano: 19 kwi 2022

Przyjęty: 22 cze 2022

DOI: https://doi.org/10.2478/amns.2022.2.0165

Słowa kluczowe
Recommendation algorithm, Logistic distribution fitting, Transition probability function, Music recommendation index

© 2023 Jianfeng Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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Music Recommendation Index Evaluation Based on Logistic Distribution Fitting Transition Probability Function

Data publikacji: 15 lip 2022

Zakres stron: 1769 - 1776

Otrzymano: 19 kwi 2022

Przyjęty: 22 cze 2022

DOI: https://doi.org/10.2478/amns.2022.2.0165

Słowa kluczoweRecommendation algorithm, Logistic distribution fitting, Transition probability function, Music recommendation index

© 2023 Jianfeng Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Figure 2

Figure 3

Figure 4

Słowa kluczowe
Recommendation algorithm, Logistic distribution fitting, Transition probability function, Music recommendation index