Music classification recommending method based on Markov prediction algorithm

Abstract

The invention discloses a music classification recommending method based on a Markov prediction algorithm. By means of the method, the Markov prediction algorithm serves as the main algorithm and is combined with an auxiliary algorithm to obtain a prediction value, and the next motion of a user is predicted through a large number of user data; the method can also be used for recommending music classifications and predicting user operation songs for music websites. The Markov prediction algorithm serves as the main algorithm and a prediction algorithm for user music behaviors; by means of the method, the next motions of the users are predicted through the Markov prediction algorithm when the users click on the music websites for music playing, collecting or downloading, and the next played music classifications of the users are predicted after the users play different classifications of music; the f value is effectively increased, and the data processing efficiency is indirectly improved.

Description

technical field [0001] The invention relates to the technical field of computer applications, in particular to a music classification recommendation method based on a Markov prediction algorithm. Background technique [0002] The music classification recommendation method based on the Markov prediction algorithm is based on the Markov algorithm to predict the user's music behavior. Currently, there are many algorithms for prediction, such as word segmentation clustering, association rules, etc. However, these single prediction algorithms cannot effectively improve the f value, and have entered the bottleneck period of prediction. [0003] At the same time, users' demand for music is increasing. Conventional websites recommend music tracks or musicians based on user click-through rates, Weibo, and news hot topics, but this cannot accurately analyze and predict users' real needs. [0004] On the one hand, huge user data cannot be quickly extracted and analyzed. On the other h...

AI Technical Summary

Problems solved by technology

However, these single prediction algorithms cannot effectively improve the f value, and entered the bottleneck period of prediction

[0003] At the same time, users' demand for music is increasing. Conventional websites recommend music tracks or musicians based on user click-through rates, Weibo, and news hot topics, but this cannot accurately analyze and predict users' real needs.

[0004] On the one hand, huge user data cannot be quickly extracted and analyzed. On the other hand, the current prediction algorithm only stays in the correlation analysis between multiple users and the analysis of the historical data of the same user. The music classification recommendation method based on the Markov prediction algorithm will improve it

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