Intelligent music recommendation system

Abstract

An embodiment of the invention discloses an intelligent music recommendation system. The system comprises an initialization module, a playing module and an adjustment module, wherein the initialization module is used for constructing a song distance network and initializing a personal song library; the playing module is used for judging a current scene, obtaining a song playing probability according to a weight value, corresponding to the scene, of a song in the personal song library, playing the song, obtaining user feedback and modifying the weight value; and the adjustment module is used for adjusting the song distance network and the personal song library. According to the embodiment of the invention, a music network with a numerical associative relationship among music is established, songs highly associated with a seed song are found in the network, and the personal song library is established by the songs; the personal song library can be intelligently adjusted, so that the song library can be increasingly closer to user preferences, the accuracy of music association values can be improved, and a personalized song library is generated for users; and the recommended songs can be automatically adjusted according to the user preferences, so that the correlation degree of recommendation is increased.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to an intelligent music recommendation system. Background technique [0002] In the field of music software: in music software such as QQ Music, Netease Cloud Music, Baidu Music, Kuwo Music and other music software, there is a function of preference recommendation, through a series of internal algorithms to find out the songs that users may like, and display them on the recommended page. There are roughly two ways to judge the user's preference type. One is to match the songs with similar tags based on the user's historical playback records and the tags (TAG) contained in the songs in the records; the other is to use the seed songs selected by the user , looking for songs with high correlation in the digital song network it built for recommendation. The recommendation methods of various music software are relatively similar. There is an existing music recommendation...

AI Technical Summary

Problems solved by technology

[0004] (1) Its algorithm for music correlation value is not comprehensive enough, so there is a gap between the calculated value and the real correlation between music

[0005] (2) Since it only looks for the song with the highest correlation value to recommend to the user each time it recommends, the songs it recommends will be discretely distributed above and below the user's favorite area during continuous use, and it is impossible to generate a personalized music library for the user the goal of

[0006] (3) Each recommendation is a similar result obtained from the same data, which lacks variability. When there is a gap between the recommended song and the user's preference, it cannot be adjusted by itself to make the next recommendation more accurate.

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