Design and Construction of Hybrid Music Recommendation System Integrating Music Gene

Abstract

In order to improve the effect of mixed music recommendation, this study combines music genes to construct a mixed music recommendation system. From the analysis of the complexity of each joint inference algorithm, GP + RKF has the highest complexity compared with the other three joint inference algorithms. Moreover, this study verifies it through the running time of the simulation experiment, using the growth method as the way of mutation. In addition, while adopting the optimal individual retention strategy, this study makes the eigenvalues of the input matrix IX all fall within the unit circle or unit circumference and makes the maximum fitness value of the individuals in the population equal to the global optimal fitness value. Finally, this study constructs an intelligent system. Through the experimental research, it can be seen that the hybrid music recommendation system based on the fusion of music genes proposed in this study has a good music recommendation effect.