Limitations of Interactive Music Recommendation Based on Audio Content.

Arthur Flexer; M. Gasser; Dominik Schnitzer

Limitations of Interactive Music Recommendation Based on Audio Content.

Arthur Flexer, M. Gasser, Dominik Schnitzer

Institute of Computational Perception

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

We present a study on the limitations of an interactive music recommendation service based on automatic computation of audio similarity. Songs which are, according to the audio similarity function, similar to very many other songs and hence appear unwontedly often in recommendation lists keep a significant proportion of the audio collection from being recommended at all. This problem is studied in-depth with a series of computer experiments including analysis of alternative audio similarity functions and comparison with actual download data.

Original language	German (Austria)
Title of host publication	Proceedings of Audio Mostly 2010
Number of pages	7
Publication status	Published - 2010

Fields of science

102 Computer Sciences
102001 Artificial intelligence
102003 Image processing
102015 Information systems
202002 Audiovisual media

Cite this

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title = "Limitations of Interactive Music Recommendation Based on Audio Content.",

abstract = "We present a study on the limitations of an interactive music recommendation service based on automatic computation of audio similarity. Songs which are, according to the audio similarity function, similar to very many other songs and hence appear unwontedly often in recommendation lists keep a significant proportion of the audio collection from being recommended at all. This problem is studied in-depth with a series of computer experiments including analysis of alternative audio similarity functions and comparison with actual download data.",

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AB - We present a study on the limitations of an interactive music recommendation service based on automatic computation of audio similarity. Songs which are, according to the audio similarity function, similar to very many other songs and hence appear unwontedly often in recommendation lists keep a significant proportion of the audio collection from being recommended at all. This problem is studied in-depth with a series of computer experiments including analysis of alternative audio similarity functions and comparison with actual download data.

M3 - Konferenzbeitrag

BT - Proceedings of Audio Mostly 2010

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