Song recommending method based on nonlinear manifold learning

Abstract

The invention belongs to the field of computer technique recommending methods, and relates to a song recommending method based on nonlinear manifold learning. The song recommending method mainly comprises the following steps of establishing a relationship model between a user and a song; performing classifying and dimensional reduction on the song; according to the relationship model obtained after dimensional reduction, recommending by the song recommending method. The song recommending method has the beneficial effect that compared with the traditional synergistic filtering algorithm, more accuracy is obtained, and a recommending plan is more reasonable.

Description

technical field [0001] The invention belongs to the field of computer technology recommending methods, and relates to a song recommending method based on nonlinear manifold learning. Background technique [0002] Since the popularization of the Internet, digital music has become an indispensable way of art consumption in people's lives, which has brought about a blowout development of the music consumption market. Gracenote, the world's largest CD database, contains more than 150 million songs , and the average person listens to only a few hundred songs a year. If the trial listening time of a song is measured in one minute, a person needs to listen to these songs for 285 years without eating or drinking; however, the number of new songs is still increasing by dozens every day. With the strength of a person's life, he cannot completely select all the songs that exist in the world, let alone listen to them all. In the face of such a huge database, how to find a favorite son...

AI Technical Summary

Problems solved by technology

If the trial listening time of a song is measured in one minute, a person needs to listen to these songs for 285 years without eating or drinking; however, the number of new songs is still increasing by dozens every day. Even with the strength of a person's life, it is impossible to select all the songs in the world, let alone listen to them all.

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