Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Information Sciences: an International Journal
The Musical Avatar: a visualization of musical preferences by means of audio content description
Proceedings of the 5th Audio Mostly Conference: A Conference on Interaction with Sound
Taking Advantage of Semantics in Recommendation Systems
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Recommendation of similar users, resources and social networks in a Social Internetworking Scenario
Information Sciences: an International Journal
Harnessing geo-tagged resources for Web personalization
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Information Processing and Management: an International Journal
Semantic description of scholar-oriented social network cloud
The Journal of Supercomputing
CooL-AgentSpeak: Endowing AgentSpeak-DL agents with plan exchange and ontology services
Web Intelligence and Agent Systems
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In this paper we give an overview of the Foafing the Music system. The system uses the Friend of a Friend (FOAF) and RDF Site Summary (RSS) vocabularies for recommending music to a user, depending on the user's musical tastes and listening habits. Music information (new album releases and reviews, podcast sessions, audio from MP3 blogs, related artists' news, and upcoming gigs) is gathered from thousands of RSS feeds. The presented system provides music discovery by means of: user profiling (defined in the user's FOAF description), context-based information (extracted from music related RSS feeds) and content-based descriptions (extracted from the audio itself), based on a common ontology (OWL DL) that describes the music recommendation domain. The system is available at: http://foafing-the-music.iua.upf.edu.