Convolution neural network-based music recommending system and method

Abstract

The invention provides a convolution neural network-based music recommending system and method. The system comprises a music user modeling module for collecting historical behavior data of a music user and constructing a preference model of the music user; a music feature modeling module for obtaining a regression model; and a recommendation algorithm module for finding music objects matched withthe preference of the music user through the regression model and recommending the music objects to the music user. According to the system provided by the invention, deep learning is applied to the recommending system, semantic differences between song features and audio signals are effectively compensated and the problems such as "cold start" and the like in collaborative filtering are avoided at the same time, so that the accuracy of the recommending system is increased; and the contradiction between low training efficiency and a high timeliness requirement is solved by adopting a convolution neural network and the historical behavior information of the user and audio acoustic features are added to the model, so that the recommendation results are more in line with the preference requirements of the user and the user experience of the recommending system is improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent recommendation, and in particular relates to a music recommendation system and a recommendation method based on a convolutional neural network. Background technique [0002] With the continuous development and application of digital multimedia technology, digital music has been appreciated and loved by the public, and people can conveniently obtain music resources through various methods such as online audition and online download. However, as the music library becomes larger and the music resources become more and more abundant, how to allow users to efficiently obtain the songs they are interested in in the vast music world has become a difficult problem. In response to this phenomenon, a personalized music recommendation system came into being. [0003] At present, the commonly used music recommendation methods mainly include content-based recommendation, collaborative filtering recommenda...

AI Technical Summary

Problems solved by technology

[0004] Although there are many types of existing music recommendation systems, the recommendation effects are uneven, and there are more or less typical problems, such as cold start, sparsity, scalability, etc.

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