Context-sensing music recommendation method based on neural network model

Abstract

The invention discloses a context-sensing music recommendation method based on a neural network model. The method comprises the step of extracting of music characteristics on the basis of the neural network model and modeling of overall situation interest of a user, extracting of context listening interest of the user and context-sensing music recommendation. According to the context-sensing music recommendation method based on the neural network model, the music characteristics and the overall situation interest characteristics of the user are extracted from a music listening sequence of the user by means of the neural network model, the context listening interest of the user is extracted from the complete listening sequence of the user, the overall situation interest of the user and the current context listening interest of the user are comprehensively considered when recommendation is conducted, therefore, the recommended music can meet the real-time requirement and preference of the user, the search cost of the user is reduced, and the satisfaction degree of the user is improved.

Description

technical field [0001] The invention belongs to the technical field of data mining and recommendation, and in particular relates to a context-aware music recommendation method based on a neural network model. Background technique [0002] With the increase of mobile communication bandwidth, the enhancement of terminal processing capability, and the development of sensing technology, more and more users listen to music through mobile terminals. Mobile users' preferences for listening to music usually change with time, space, weather, and physical conditions. Traditional music recommendation systems are no longer suitable for personalized mobile network services. In recent years, context-aware music recommendation systems have become an emerging research area by introducing contextual information into the recommendation system. In the research, it is found that integrating contextual information into the recommendation system is equivalent to extending the traditional "user-i...

AI Technical Summary

Problems solved by technology

[0004] However, the existing technology only considers the user's context information in the matching process of music and users, and lacks an in-depth analysis of the music content. different degrees of preference, that is, different music is differentiated by the user attributes of music, thus ignoring the contextual attributes of music itself as a type of multimedia file

This recommendation method is too subjective, which reduces the coupling between users and music, thus affecting the accuracy of the recommendation system

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