NDLI: Spotting out emerging artists using geo-aware analysis of P2P query strings

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Spotting out emerging artists using geo-aware analysis of P2P query strings

Content Provider	ACM Digital Library
Author	Koenigstein, Noam Tankel, Tomer Shavitt, Yuval
Abstract	Record label companies would like to identify potential artists as early as possible in their careers, before other companies approach the artists with competing contracts. The vast number of candidates makes the process of identifying the ones with high success potential time consuming and laborious. This paper demonstrates how datamining of P2P query strings can be used in order to mechanize most of this detection process. Using a unique intercepting system over the Gnutella network, we were able to capture an unprecedented amount of geographically identified (geo-aware) queries, allowing us to investigate the diffusion of music related queries in time and space. Our solution is based on the observation that emerging artists, especially rappers, have a discernible stronghold of fans in their hometown area, where they are able to perform and market their music. In a file sharing network, this is reflected as a delta function spatial distribution of content queries. Using this observation, we devised a detection algorithm for emerging artists, that looks for performers with sharp increase in popularity in a small geographic region though still unnoticable nation wide. The algorithm can suggest a short list of artists with breakthrough potential, from which we showed that about 30% translate the potential to national success.
Starting Page	937
Ending Page	945
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1402002
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	P2p queries Emerging artists
Content Type	Text
Resource Type	Article

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