Network music aggregation recommendation method based on label digraphs

Abstract

The invention discloses a network music aggregation recommendation method based on label digraphs, and belongs to the field of network music aggregation. The network music aggregation recommendation method based on the label digraphs can effectively solve the problem that a user classification preference sequence cannot be reflected in an existing traditional label classification recommendation method. According to the network music aggregation recommendation method based on the label digraphs, a quaternary relation formed by users, labels, music and a cognition sequence is taken into adequate consideration; the relational network is utilized for further improving music recommendation accuracy and the degree of satisfaction of the network users. According to the technology, network music features and user interest features are described through the digraphs, and a music feature digraph set is divided into a plurality of digraph clusters, so that the digraphs in each cluster are isomorphic to the greatest extent while the digraphs in different clusters are different to the greatest extent (representing the difference between the digraphs). At the time of similarity matching, it is not needed that the whole music feature digraph set is searched, most of suitable digraphs of a target can be found through inquiries from a plurality of digraph clusters with the highest similarity to a target user interest digraph, and therefore the purpose of recommending music to the music network users quickly and accurately can be achieved.

Description

technical field

[0001] The invention belongs to the field of network music aggregation based on music tags, and is used to meet personalized music requirements such as recommending network music for users in a network environment. Tag-based music recommendation techniques bring new expressiveness to online music aggregation services, but these studies mainly revolve around flat classification tags. The problem with the flat classification labeling technology is that the labels are discrete, that is, the labels are in a parallel and disordered relationship, which cannot reflect the user's classification preference for music labeling, and cannot reflect the user's cognitive order when classifying. For more accurate music recommendation, the problem of plane classification must be improved. The invention provides a tag-directed graph-based network music aggregation and recommendation method. Through this method, network music can be recommended to users more effectively and acc...

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