Music recommendation method and device based on context sensing

Abstract

The invention discloses a music recommendation method and device based on context sensing. The method comprises the following steps: obtaining the context information of current equipment; according to the corresponding relationship of the context information and music characteristics, obtaining the music characteristics corresponding to the context information; and according to the music characteristics, obtaining the music, which is matched with the music characteristics, of a server side, and recommending the music to the music player of current equipment to be played. The device comprises an information acquisition module, a characteristic acquisition module and a music acquisition module. The context information of the equipment is associated with the music characteristics, the music characteristics of recommended music are determined according to the context information of the current equipment, and therefore, the method gets rid of personal experiences, does not depend on personal users and is suitable for general personalized service.

Description

technical field [0001] The present invention relates to the field of multimedia technologies, in particular to a context-aware-based music recommendation method and device. Background technique [0002] With the rapid development of interactive technology and the popularity of mobile applications, smartphones are gradually replacing desktop computers as the main medium for users to access Internet content. Convenient and ubiquitous network connections enable users to enjoy various network resources anytime and anywhere. The resources that these users are interested in may be entertainment, leisure, social interaction, learning, etc. However, among these many resources, music is a typical resource that can attract countless users' interests. [0003] While listening to music with mobile devices is more convenient than the traditional way of listening to music, there are also many challenges. For example, selecting a song to play on a mobile phone requires the user's attenti...

AI Technical Summary

Problems solved by technology

The third point is that the process of users listening to music actually includes the process of user interaction. Users will have explicit feedback (user ratings) or implicit feedback on music. Therefore, how to use user feedback is a challenging task.

However, every time the model records a song listening or a new song is added to the database, the sorting of the entire song list needs to be recalculated, which is very time-consuming and inefficient

These context-aware music recommendation systems use artificial experience to mark music. However, this method depends on individual users and may not be suitable for personalized services. Therefore, how to map certain information through context-aware information The song is also very challenging

[0007] Existing methods mainly recommend music based on the common preferences of the public, and lack of consideration of individual user behavior. Even if individual user behavior is considered, it still recommends songs that most people like to listen to under this behavior, without considering the uniqueness of individual users. music preferences

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