A Semantically Enhanced Tag-Based Music Recommendation Using Emotion Ontology

In this paper, we propose a semantically enhanced tag-based approach to music recommendation. While most of approaches to tag-based recommendation are based on tag frequency, our approach is based on semantics of tags. In order to extract semantics of tags, we developed the emotion ontology for music called UniEmotion, which categorizes tags into positive emotional tags, negative emotional tags, and factual tags. According to the types of the tags, their weights are calculated and assigned to them. After then, user profiles using the weighted tags were generated and a user-based collaborative filtering algorithm was executed. To evaluate our approach, a data set of 1,100 users, tags which they added, and artists which they listened to was collected from last.fm. The conventional track-based recommendation, the unweighted tag-based recommendation, and the weighted tag-based recommendation are compared in terms of precision. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.