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

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Presentation transcript:

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

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 Copyright Act ( ) –Main service Custom-build user’s own radio 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: Inside Pandora: Web Radio That Listens to You, O’Reilly digitalmedia

Musicovery Briefing –Music tailored to your mood –Developed in 2006 (2005) –Main service Custom-build user’s own 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 copyright infringement lawsuit ( ) –Main service online custom-build user’s own radio stations Programmed radio stations Music videos and interviews 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 ( ) –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 InterfaceAlgorithmPresent Pandora Artist Song Similarity Implicit feedback Radio stations Musicovery Mood Tempo Genre / Epochs Content-based filtering Visualized playlists Launchcast Genre Artist Album Group Co-occurrence Collaborative filtering Content-based filtering Explicit rating Radio stations Last.fm Artist Album Group Social-related Co-occurrence Collaborative filtering Content-based filtering Scrobbling Radio stations Similar taste users