A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
The state of the art in tag ontologies: a semantic model for tagging and folksonomies
DCMI '08 Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
MusicBox: personalized music recommendation based on cubic analysis of social tags
IEEE Transactions on Audio, Speech, and Language Processing
Music emotion classification and context-based music recommendation
Multimedia Tools and Applications
Categorising social tags to improve folksonomy-based recommendations
Web Semantics: Science, Services and Agents on the World Wide Web
Generation of tag-based user profiles for clustering users in a social music site
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
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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.