Distributed, real-time computation of community preferences
Proceedings of the sixteenth ACM conference on Hypertext and hypermedia
Inferring similarity between music objects with application to playlist generation
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
An approach for combining content-based and collaborative filters
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
A Semantic Web ontology for context-based classification and retrieval of music resources
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A new approach for combining content-based and collaborative filters
Journal of Intelligent Information Systems
A probabilistic music recommender considering user opinions and audio features
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
A Case-Based Song Scheduler for Group Customised Radio
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
DJ-Boids: flocks of DJ's to program internet multichannel radio stations
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
Extending a web browser with client-side mining
APWeb'03 Proceedings of the 5th Asia-Pacific web conference on Web technologies and applications
A probabilistic model for music recommendation considering audio features
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Case-based sequential ordering of songs for playlist recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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In recent years, the popularity of online radio has exploded.This new entertainment medium affords an opportunitynot available to conventional broadcast radio: theinstantaneous listening audience can be known, or whatis more important, the musical tastes of the current listeningaudience can be known. Thus, it is possible in thenew medium to tailor the playlist in real-time to the musicaltastes of the listening audience. We discuss a method,termed flycasting, for using collaborative filtering techniquesto generate a playlist in real-time based on the requesthistories of the current listening audience. We alsodescribe a concrete implementation of the technique.