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

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.

Keywords

• recommendation algorithm
• logistic distribution fitting
• transition probability function
• music recommendation index
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