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17 MARCH 2014

The Pandora Music Genome Project

"We believe that each individual has a unique relationship with music–no one else has tastes exactly like yours. So delivering a great radio experience to each and every listener requires an incredibly broad and deep understanding of music. That's why Pandora is based on the Music Genome Project, the most sophisticated taxonomy of musical information ever collected. It represents over ten years of analysis by our trained team of musicologists, and spans everything from this past Tuesday's new releases all the way back to the Renaissance and Classical music.

Each song in the Music Genome Project is analyzed using up to 450 distinct musical characteristics by a trained music analyst. These attributes capture not only the musical identity of a song, but also the many significant qualities that are relevant to understanding the musical preferences of listeners. The typical music analyst working on the Music Genome Project has a four–year degree in music theory, composition or performance, has passed through a selective screening process and has completed intensive training in the Music Genome's rigorous and precise methodology. To qualify for the work, analysts must have a firm grounding in music theory, including familiarity with a wide range of styles and sounds.

The Music Genome Project's database is built using a methodology that includes the use of precisely defined terminology, a consistent frame of reference, redundant analysis, and ongoing quality control to ensure that data integrity remains reliably high. Pandora does not use machine–listening or other forms of automated data extraction.

The Music Genome Project is updated on a continual basis with the latest releases, emerging artists, and an ever–deepening collection of catalogue titles.

By utilizing the wealth of musicological information stored in the Music Genome Project, Pandora recognizes and responds to each individual's tastes. The result is a much more personalized radio experience – stations that play music you'll love – and nothing else."

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TAGS

analysing dataappeal • attributes • automated data extraction • characteristicsdata analysisdata gathering instruments • data integrity • databasedescriptive labels • ersonalised radio experience • frame of reference • individual preference • individual taste • internet radio • listener preference • machine-listening • metricsmusic • music analyst • Music Genome Project • music taste • music theory • musical characteristicsmusical identitymusical information • musical preferences • musicological information • musicologist • Pandora Radiopersonal taste • precisely terminology • qualities • quality control • radio • radio experience • redundant analysis • relatednesssegmentationsongtaste (sociology)taxonomy • unique taste • user behavioursuser segmentation

CONTRIBUTOR

Simon Perkins
04 JANUARY 2014

An introduction to recommender systems

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TAGS

advogato.org • affinity analysisalgorithmic filtersAmazon.comautomatic predictions • collaborative filtering • collaborative filtering approach • correlationsdata matchingdata miningecho chamberfavourite things • FilmTrust • information filtering • information filtering system • information patterns • interests • Jennifer Golbeck • large datasetsLast.fm • moleskiing.it • Pandora Radiopersonal tastepersonalised suggestionsprediction • process of filtering • rating systemrecommendationrecommendation enginerecommendation platform • recommendation system • recommendation systems • recommender systems • relatednessrelationships between individualsserendipitysimilaritysimilarity machinesimilitude • trust metric • trust-based recommender systems • user datauser preferences

CONTRIBUTOR

Simon Perkins
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