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

A hybrid model and recommendation method technology, applied in biological neural network models, special data processing applications, instruments, etc., to achieve the effects of enhancing robustness, improving defense attack capabilities, and improving diversity

Pending Publication Date: 2021-08-06
LIAONING TECHNICAL UNIVERSITY
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  • Application Information

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

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Embodiment Construction

[0058] The specific embodiment of the present invention is described in detail below in conjunction with accompanying drawing, and it is as a part of this specification, and the principle of the present invention is illustrated through the embodiment, and other aspects, features and advantages of the present invention will become clear at a glance through this detailed description. In the figures referred to, the same or similar parts in different figures are denoted by the same reference numerals.

[0059] Such as Figure 1 to Figure 6 As shown, the music recommendation method based on the time resident and state resident hybrid model of the present invention comprises the following steps:

[0060] Step 1. Collect music audio, user's historical listening records, rating information, song label collection, music playback times and playback time, perform noise reduction, pre-emphasis, and frame windowing on the audio signal, and perform music resource files Preprocessing to ob...

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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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Claims

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Application Information

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Owner LIAONING TECHNICAL UNIVERSITY
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