Music Recommendation with High-dimensional Features

Thesis Type Master
Thesis Status
Currently running
Student Christoph Kirchner
Thesis Supervisor
Research Field

Music recommender systems as part of music streaming platforms such as Spotify or Deezer are trained on large datasets containing user-song interaction logs. Additionally, each song can be represented via high-level features to improve the quality of the recommendations. However, there are many possibilities for how to represent a song and which meta-data to include, leading to the curse of dimensionality for feature-rich datasets. This thesis aims to find suitable methods for feature dimensionality reduction including the analysis of feature importance and to benchmark different approaches on a feature-rich music recommender dataset.