Matches in ScholarlyData for { ?s ?p Music is not a patio chair or a cutlery set, and it should not be recommended the same way. Music is a highly complex signal that is part of our cultural framework and has a prominent place in our everyday life. Yet dealing with the millions of songs we have access to and finding the ones that will enliven our day is essential. What could once be solved by advice from one’s best friends has evolved into a large-scale internet-era problem, and the music information retrieval community needs to step up to the challenge. We introduce the Million Song Dataset Challenge to improve the state-of-the-art and unify the community behind a common evaluation. The goal is to predict the songs a user will listen to, given the user’s listening history and full information (meta-data, content analysis) of all songs. We explain the need for such a contest, the data we use, our goals and design choices, and present baseline experimental results using simple, off-the-shelf recommendation algorithms.. }
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- 10 abstract "Music is not a patio chair or a cutlery set, and it should not be recommended the same way. Music is a highly complex signal that is part of our cultural framework and has a prominent place in our everyday life. Yet dealing with the millions of songs we have access to and finding the ones that will enliven our day is essential. What could once be solved by advice from one’s best friends has evolved into a large-scale internet-era problem, and the music information retrieval community needs to step up to the challenge. We introduce the Million Song Dataset Challenge to improve the state-of-the-art and unify the community behind a common evaluation. The goal is to predict the songs a user will listen to, given the user’s listening history and full information (meta-data, content analysis) of all songs. We explain the need for such a contest, the data we use, our goals and design choices, and present baseline experimental results using simple, off-the-shelf recommendation algorithms.".