Algorithmic guidance on cultural good consumption (online/offline)

Computer Science: One possible internship

Context

The effects of recommendation algorithms on the access to information and cultural goods is at the center of a growing debate, which aims at assessing whether they rather contribute to enlarge or to restrain the horizon of users with respect to their “organic” behavior, i.e. absent algorithms.

Goals

This internship topic focuses on the impact of algorithmic guidance on cultural good consumption, specifically musical goods. It aims at addressing the following question: to what extent could we say that traditional musical “recommendation” (radios, music libraries, record stores) is more or less diversified and/or serendipous than algorithmic recommendation (e.g., on YouTube or leading music streaming platforms)? In other words, we aim at appraising the discrepancy between online and offline guidance.

The intern would benefit from significant autonomy in the design and realization of the empirical measures, protocol, and result analysis. Besides, fully anonymized data coming from a leading music streaming platform would be readily available from the beginning of the internship.

Intended audience

We open one internship under the joint supervision of Camille Roth and Jérémie Poiroux. Applicants should ideally be achieving a master’s degree in computer science and related fields; modeling and/or online data collection skills are desirable.

Practical Details

  • The internship could last between three and six months;
  • The internship is based in Berlin at the Centre Marc Bloch;
  • The intern should have working proficiency in either English, French or German;
  • The internship allowance is fixed by law at an amount of about 500 euros on a 38 hours basis.

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