In the music streaming industry, music recommendation is a key component to retain and attract users. Suggesting relevant personalized songs, artists, albums, or playlists helps users actively explore the vast and mostly unknown musical landscape. It is also central to all enjoyable passive experiences relying on generated and personalized content.
To recommend music on Deezer, our team aims to learn the musical preferences of each user, by processing and analyzing their listening history on the service. However, associating users with “fixed” musical tastes would be limiting. Indeed, depending on the context, some users will have different preferences and will listen to music differently. For instance, listening practices can evolve depending on the current activity or the time of the day. In practice, we observe that some of our Deezer users do have very distant preferences depending on the context. For instance, some users prefer to listen to classical music in context A, and to heavy metal in context B. The scientific literature, as well as some of our previous internal investigations, have shown that these contextual aspects can extensively impact the way the same recommendation will be perceived by users.
What you will do:
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