Mining music from large-scale, peer-to-peer networks

Yuval Shavitt; Ela Weinsberg; Udi Weinsberg

doi:10.1109/MMUL.2011.13

Mining music from large-scale, peer-to-peer networks

Yuval Shavitt, Ela Weinsberg, Udi Weinsberg

Department of Electrical Engineering - Systems

Tel Aviv University

Research output: Contribution to journal › Article › peer-review

Abstract

Using song files shared by users in the Gnutella network, the authors present a method for constructing a song-similarity graph to create scalable and efficient applications.

Original language	English
Article number	5720675
Pages (from-to)	14-23
Number of pages	10
Journal	IEEE Multimedia
Volume	18
Issue number	1
DOIs	https://doi.org/10.1109/MMUL.2011.13
State	Published - Jan 2011

Keywords

Data mining
IEEE Multimedia
information retrieval
multimedia
peer-to-peer networks
recommendation systems

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Media Technology
Hardware and Architecture
Computer Science Applications

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10.1109/MMUL.2011.13

Cite this

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title = "Mining music from large-scale, peer-to-peer networks",

abstract = "Using song files shared by users in the Gnutella network, the authors present a method for constructing a song-similarity graph to create scalable and efficient applications.",

keywords = "Data mining, IEEE Multimedia, information retrieval, multimedia, peer-to-peer networks, recommendation systems",

author = "Yuval Shavitt and Ela Weinsberg and Udi Weinsberg",

note = "Funding Information: We thank Tomer Tankel for providing the data used in this article, and sharing valuable ideas for its successful analysis. This research was supported in part by the Israel Science Foundation center of excellence program (#1685/07) and by the Ministry of Trade and Industry, Magnet program through the NeGeV Consortium.",

year = "2011",

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KW - recommendation systems

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Mining music from large-scale, peer-to-peer networks

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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