Build your own Playlist Recommender System with Python using your GDPR Data - by Marcel Kurovski

EuroPython 2022 - Build your own Playlist Recommender System with Python using your GDPR Data - presented by Marcel Kurovski
[Liffey Hall 1 on 2022-07-15]
Personalized Playlist Recommendations on Spotify are great - some of them let us discover new songs, some others help us to rediscover songs. However, rediscovery seems to be limited on the more recent past, i.e. going only a month backwards. This is a problem if you like to rediscover some of your favorite songs you might have listened to a longer while ago. Sometimes we add them to our ""liked songs"" where they likely fade away. However, you once explicitly declared those tracks as favorites. So, what is it that we can do about this missing piece in personalized playlist recommendations?
Well, the first thing we do is to request our personal usage data from Spotify according to GDPR. Second, we analyze and enrich it with track audio features offered by Spotify’s rich Web API. We derive the music taste profile of ourselves from 12 months of streaming history and use this taste profile to retrieve favorite songs we haven’t listened to for more than a year. In my talk, I present you the Python package I build for this purpose, possible extensions and enable you to create your own personalized playlist to rediscover your past!
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