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A music recommendation method for large-scale music library on a heterogeneous platform

  • Beihang University
  • Aviation Institute

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Currently, music recommendation system is a research focus in music information retrieval and a typical system can handle millions of music in real time. However, online music libraries have exceeded ten-million magnitudes, such as Amazon MP3, which results in mismatching between music recommendation systems and music libraries. Thus, this paper presents a music recommendation method for retrieving the large-scale music library on a heterogeneous platform. Based on the music similarity algorithm, by combining the indexing mechanism with GPU hardware acceleration, we further enhance the processing scale of the proposed method. Experiments show that, without lowering the retrieval accuracy, the proposed music recommendation method has the ability to handle ten-million magnitude libraries online in a single server.

Original languageEnglish
Title of host publicationAlgorithms and Architectures for Parallel Processing - 14th International Conference, ICA3PP 2014, Proceedings
PublisherSpringer Verlag
Pages472-482
Number of pages11
EditionPART 1
ISBN (Print)9783319111964
DOIs
StatePublished - 2014
Event14th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2014 - Dalian, China
Duration: 24 Aug 201427 Aug 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8630 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2014
Country/TerritoryChina
CityDalian
Period24/08/1427/08/14

Keywords

  • GPU-accelerated
  • Large-scale
  • Music recommendation
  • Music similarity algorithm

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