Data School

Personalisation for (public) media

Recommender systems are an integral part of our daily media consumption: they compile playlists on Spotify, suggest movies on Netflix, and select (news) content for personalized social media feeds on e.g., Facebook or Twitter. 

In the age of information overload, recommender systems provide orientation and help users with making choices. Through data collection and statistical modelling, the underlying algorithms identify and present content that is considered most “relevant” to users. 

However, recommender systems are not objective observers and/or advisors; they carry particular norms and values that their creators consciously -and unconsciously- impart during the development and deployment of algorithms. 

These factors and their social impact are highly-context dependent. For example, recommender systems are often at the centre of discussions about political polarisation on digital platforms and have been associated with the reinforcement of “tunnel vision” among users by leading them into content funnels that may reduce exposure to diversity. 

This course centers on the question: how can recommender systems implement public values (e.g., trust, autonomy, diversity, sustainability)?

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