Listening sequence and metadata based context-sensing music recommendation method

Abstract

The invention discloses a listening sequence and metadata based context-sensing music recommendation method. The method comprises the steps of S1 extracting music characteristics based on a music listening sequence and music metadata; S2 extracting a global interest and a context listening interest of a user; and S3 performing context-sensing music recommendation. According to the method, the music characteristics are extracted from the music listening sequence and the music metadata of the user by utilizing a neural network model, then the global interest and the context listening interest of the user are extracted from a complete listening sequence and a recent listening sequence of the user, and finally the global interest and the current context listening interest of the user are comprehensively considered, so that recommended music can meet real-time demands and preferences of the user, the search cost of the user can be reduced, and the satisfactory degree of the user can be increased.

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 listening sequences and metadata. 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 tradition...

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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