music recommendation on-line survey presented by daniel wu & gordon chang 2007.12.28
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Music RecommendationOn-line Survey
Presented by Daniel Wu & Gordon Chang
2007.12.28
Social Media Website
Pure Recommendation
Survey Introduction• Purpose
– Analyze industry trend. Seek improvement that could be made on music
recommendation systems.
• Provider surveyed– Pandora– Musicovery– Launchcast– Last.fm
• Dimension– Briefing– Recommendation method– Database building– Service provided
Pandora
• Briefing– Founded in 2005 (2000)
– Registered Users• 2.5m+ (as of 2006)
– Block non-US listener due to the Digital Millennium C
opyright Act (2007.05)
– Main service• Custom-build user’s own r
adio stations
• Recommendation– Similarity– Implicit feedback
(thumbs up/down, time)
• Database building– 500,000+ songs – 42+ professionals– 200+ features
Pandora
• Find music– Favorite artist
– Favorite song
• Listening page– Artist background– Songs descriptions– User feedback
(thumbs up/down, fair)
• Welcome self-submission
Services
Src: http://digitalmedia.oreilly.com/2006/08/17/inside-pandora-web-radio.html
Inside Pandora: Web Radio That Listens to You, O’Reilly digitalmedia
Musicovery
• Briefing– Music tailored to your
mood– Developed in 2006 (20
05)
– Main service• Custom-build user’s ow
n radio stations
• Recommendation– Similarity– Implicit feedback– Content-based filtering
• Database building– Professional grouping
(guess)
Musicovery
• Radio mode– Personal radio
• Find music– Mood– Genre– Epoch– Tempo / Dance– Favorite artist– Favorite songs– Hit / nonHit / Discovery
• Listening page– Album Cover– Artist– Song– Amozon / Ebay /
iTune
• Platform for new music– Discovery
Services
Launchcast
• Briefing– Began in the late 1990s by
LAUNCH Media – Acquired by Yahoo!: $12m
(2001)– Defeated Sony BMG in a c
opyright infringement lawsuit (2007.04.27)
– Main service• online custom-build user’s
own radio stations• Programmed radio station
s• Music videos and intervie
ws
• Recommendation– Co-occurrence
(similar artists)– Collaborative filtering– Content-based filtering– Explicit rating
• Database building– Personal rating systems– Collaborative initialization– 2 million+ songs
Launchcast
• Radio mode– Personal radio– Programmed radio– Member’s radio– Similar artist radio– Artist fan radio
• Find music– Artist– Album– Lyrics– Songs– genre
• Listening page– Song– Artist– Album– Selected Reason
• Platform for new artists
• User finder– Music taste– Music influence
Services
Last.fm
• Briefing– Founded in 2002– Active users: 15m+ – Bought by CBS: $280m (20
07.05.30)– Main service
• custom-build user’s own radio stations
• connect listeners with similar music tastes
• Recommendation– Co-occurrence
(similar artists)– Collaborative filtering– Content-based filtering
• Database building– Scrobbling– Listening history importing– Collaborative initialization
Last.fm
• Radio mode– Personal radio– Neighbor radio– Loved track radio– Group radio– Similar artist radio– Artist fan radio– Tag radio
• Find music– Artist– Album– Tag– Username– Group– Ranking
• Listening page– Artist background– Similar artists– User feedback
• Platform for new artists
• User finder– Gender– Age range– Profile keyword search– Music taste
Services
Layers of Music Recommendation
• Layers
– Music search interface • by artist, song, genre, PAD…
– Music recommendation algorithm• Content-based, collaborative filtering…
– Music search result presentation • on-line radio station, playlist, single song…
• Improvements could be made in each layer
Survey Summary
Interface Algorithm Present
Pandora
ArtistSong
SimilarityImplicit feedback
Radio stations
Musicovery
MoodTempoGenre / Epochs
Content-based filtering Visualized playlists
Launchcast
Genre ArtistAlbumGroup
Co-occurrenceCollaborative filteringContent-based filteringExplicit rating
Radio stations
Last.fm
ArtistAlbumGroupSocial-related
Co-occurrenceCollaborative filteringContent-based filteringScrobbling
Radio stationsSimilar taste users
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