To graduate from the first year of my master's education and demonstrate my ability to independently plan and execute an interaction design project, I ​explored users' algorithm experience with music recommendation systems in a mainly remote research project. In individual focus user sessions, I introduced basic principles of machine learning to then understand the existing relationship to algorithmic music recommendations. Several people tested the Figma prototype that emerged from user insights and an expert interview, which lead to even further insights.

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_ abstract: After decades of black-boxing the existence of algorithms in technologies of daily need, users lack confidence in handling them. This thesis study investigates the use situation of intelligent music recommendation systems and explores how understandability as a principle drawn from sociology, design, and computing can enhance the algorithmic experience. In a Research-Through-Design approach, the project conducted focus user sessions and an expert interview to explore first-hand insights. The analysis showed that users had limited mental models so far but brought curiosity to learn. Explorative prototyping revealed that explanations could improve the algorithmic experience in music recommendation systems. Users could comprehend information the best when it was easy to access and digest, directly related to user behavior, and gave control to correct the algorithm. Concluding, trusting users with more transparent handling of algorithmic workings might make authentic recommendations from intelligent systems applicable in the long run.

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As part of the ACM RecSys perspectives workshop, I shared my work in the form of a paper (co-authored by my supervisor Maliheh Ghajargar) and a video with other participants to discuss current development on recommender system evaluation.