MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Spotting out emerging artists using geo-aware analysis of P2P query strings
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring the music similarity space on the web
ACM Transactions on Information Systems (TOIS)
Auralist: introducing serendipity into music recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
Towards automatic retrieval of album covers
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
“Reinventing the Wheel”: A Novel Approach to Music Player Interfaces
IEEE Transactions on Multimedia
A survey of music similarity and recommendation from music context data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
The neglected user in music information retrieval research
Journal of Intelligent Information Systems
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Music retrieval systems that take into account the user's taste and information or entertainment need when building the results set to a query are of vital interest for academia, industry, and the passionate music listener. Unfortunately, preliminary attempts to incorporate such aspects have been rather sparse so far. Focusing on the problem of music recommendation, we therefore present a new model that combines several factors we deem to be important for personalizing retrieval results: similarity, diversity, popularity, hotness, recentness, novelty, and serendipity. We further propose different ways to measure the corresponding aspects and, where available, point to literature for a more detailed elaboration of the corresponding measures. In addition, we propose the use of social media mining techniques to address the problem of estimating popularity and hotness in a geo-aware manner.