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Research about recommendation systems has increased due to the amount of information that it is available to individuals. In the music context these systems help the individual to filter and discover new songs according the individual's taste. Most of the business music companies use a recommendation system, based on the characteristics of a song listened by an individual, but a group recommendation system is still underexplored. For a shared environment when there is music, the songs selection will be more efficient if a group recommendation system is used. The goal of this project is to develop a music recommendation for a group that, is sharing the same environment, taking into consideration the context. For this reason, in this work we will employ the Spotify API to recover the data of playlists that were listened by an individual, collecting its preferences and adding them to the others individuals playlists.
A simple AAU report template (2015-05-08 v. 1.2.0),
Copyright 2010-2015 by Jesper Kjær Nielsen <jkn@es.aau.dk>
This is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You can find the GNU General Public License at <http://www.gnu.org/licenses/>.
This template was downloaded from the original author's page at http://kom.aau.dk/~jkn/latex/latex.php#reporttemplate.
Neural Networks for Engineering Students
Read the top first. Elon Musk thinks this is more dangerous than nukes. Show a little respect, but don't be afraid.