music recommendation on-line survey presented by daniel wu & gordon chang 2007.12.28

12
Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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Page 1: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

Music RecommendationOn-line Survey

Presented by Daniel Wu & Gordon Chang

2007.12.28

Page 2: Music Recommendation On-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

Page 3: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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

Page 4: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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

Page 5: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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)

Page 6: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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

Page 7: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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

Page 8: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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

Page 9: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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

Page 10: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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

Page 11: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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

Page 12: Music Recommendation On-line Survey Presented by Daniel Wu & Gordon Chang 2007.12.28

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