Personalized music tagging using ranking on hypergraphs

Social tagging enables users of social media sharing platforms to annotate multimedia items by employing arbitrary keywords (i.e., tags), which describe better the multimedia content. Several applications, such as personalized multimedia recommendation or music genre classification, to name a few, benefit from tagging. Clearly, tagging aims at bridging the semantic gap between human concepts and content retrieval exploiting low-level features extracted from the multimedia. Here, the problem of personalized tag recommendation is addressed in a “query and ranking” manner on hypergraphs. This way, the relationships between the different object types, such as user friendships, user groups, music tracks and tags are captured and tags are recommended for a certain track to a user. Ranking on hypergraphs is studied by enforcing either ℓ₂ norm regularization or group sparsity. Experiments on a dataset collected from Last.fm demonstrate a promising tag recommendation accuracy.