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October 18th, 2008
Music discovery on the net Barcamp3, Berlin
Petar Djekic
From phonograph to widgets
Phono
1890
Radio Phono
1920
TV Car Audio
Radio Phono
1930
Portable TV
Car Audio Radio
HiFi system
1950
PC Portable
TV Car Audio
Radio HiFi system
1980
Web PC
Portable TV
Car Audio Radio
HiFi system
1990
Widget Mobile Web PC
Portable TV
Car Audio Radio
HiFi system
2000
Source: own, Wikipedia ’08
Yet still..
Source: Apple 2008, Forrester Research 2008, IPSOS 2006
„There is are an average of 700 songs stored on a U.S. music downloader’s player.“
„Average MP3 player only 57% full“
„iPod classic can hold up to 30,000 songs“
4 Music Discovery
“The only bad thing about MySpace is that there are 100,000 bands and no filtering. I try to find the bands I might like but often I just get tired of looking.”
15 year old student, IFPI focus group research, July 2007
“A wealth of information creates a poverty of attention”
Herbert A. Simon, Nobel prize winning economist
5
Many places, similar technologies
Music Discovery
Human behaviour: Recommendations are based on behaviour, e.g., Collaborative filtering using listening or purchase habits
Human annotation: Recommendations are based on annotations and expertise, e.g., ratings, tags, classification into genres, editorial content
Content analysis: Recommendations are based on characteristics of the content itself, e.g., sound density, vocals, tempo, sound color, instruments, volume, dynamics
Recommendation technologies: Overview
Source: audiobaba
„Freakomendations“: Variety
Source: Paul Lamere, last.fm
„Freakomendations“: Manipulation
Source: iTunes Genius
„Freakomendations“: Cold-start
Source: mufin.com
„Freakomendations“: Relevance
Relevance: How good does the content suit my taste? How about mood and expectations?
Scalability: Indexing of existing content libraries and new releases (cold-starts)
Objectivity: Manipulation of rankings, consistency of recommendations
Variety: Variety of recommendations (Beatles-problem); connection between variety and content available
Privacy: Who owns YOUR data?
Explanation: Why was something recommended?
Portability: How about mobile devices, MP3 players
Recommendation technologies: Issues
Human annotation/behaviour
Content analysis
MusicBrainz: similar artists, tags, meta data, CC / PD license
Yahoo! Music: similarities, charts, ratings, meta data, REST webservice, max. 5000 queries/day
Last.fm: similarities, tags, ratings, meta data, REST webservice, free for non-commercial use
Echo.nest: sound analysis, recommendations, custom HTTP webservice,
audiobaba: similarities, custom HTTP webservice, max. 1 query/sec
Mash it up now! <resources>
Mash it up now! <resources>
Matching
Identifier: MusicBrainz, ISRC, All music guide
Meta data: G’n’R, GunsNRoses, Guns N’ Roses…
Acoustic fingerprints: Standards?
Youtube
Imeem Media Platform, yahoo
Seeqpod, skreemr
Radio stream
Full-track
Books David Jennings (2006) Net, Blogs, and Rock‘n‘Roll
David Huron (2008) Sweet Anticipation: Music and the Psychology of Expectation
Papers Kim, J., and Belkin, N. J. (2002). Categories of music description and search terms and phrases used by non-music experts, http://
ismir2002.ismir.net/proceedings/02-FP07-2.pdf
Tintarev, N. (2007), A Survey of Explanations in Recommender Systems, http://www.csd.abdn.ac.uk/~ntintare/
TintarevMasthoffICDE07.pdf
Mobasher, B. et al (2007) Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm
Robustness, http://maya.cs.depaul.edu/~mobasher/papers/mbbw-acmtoit-07.pdf
Conferences The International Conferences on Music Information Retrieval and Related Activities, ISMIR, http://www.ismir.net/
ACM Recommender Systems, RecSys, http://recsys.acm.org
Blogs Duke Listens!, http://blogs.sun.com/plamere/
Recommendations, again
