Music recommendation method based on time residence and state residence mixed model

Abstract

The invention discloses a music recommendation method based on a time residence and state residence mixed model, which comprises the following steps: expanding five elements of a traditional hidden Markov model into seven elements, calculating and predicting music which is possibly interested by a user at the next moment in behavior information of interaction between the user and the music, and forming a user preference list. A user preference vector is extracted from a user score matrix by using an SVD matrix decomposition algorithm, meanwhile, potential features of song audio signals are mined by using a convolutional neural network, and song tag features are extracted by combining an embed layer with a long short-term memory artificial neural network LSTM, the obtained music potential feature vector and the user preference vector are trained, and the two are trained at the same time to obtain a candidate list; and finally, the two lists are reordered by utilizing a rating function to obtain a diversified optimal recommendation list. According to the method, the recommendation accuracy is improved, information overload is effectively solved, meanwhile, the problems of cold start and the like in a recommendation system are relieved, and the recommendation diversity and robustness are improved.

Description

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Problems solved by technology

However, in recent years, there has been no recommended system that has been studied, which can not only

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