Data Analysis

Comparative analysis of similarity and diversity metrics for recommender systems

Idea

Similarity and diversity are essential for recommender systems, as they help predict items a user may like. Research has come up with a broad portfolio of similarity and diversity metrics. The core of this project is to perform a comparative analysis of such metrics. The idea is to run computational experiments (offline study) and analyze how congruent (or different) the indications for similarity or diversity are for a set of metrics.

Supervisor: Christine Bauer
24 July 2024
Comparative analysis of similarity and diversity metrics for recommender systems
Diurnal fluctuations in musical preference

Idea

Diurnal patterns guide our everyday life. The basic idea of this thesis project is to analyze listening logs (from online streaming services) for diurnal patterns.

Relevant references:

Potentially relevant datasets:

Supervisor: Christine Bauer
24 July 2024
Diurnal fluctuations in musical preference
The influence of music recommender systems on the diversity of music consumed

Idea

Music recommender systems are integral to popular music platforms, such as Spotify, Pandora, and YouTube. Recommender systems help to filter the tremendous amount of content available, and - with it - they also influence to a certain degree what kind of content users consume. The central question of this project is: How do music recommender systems influence the diversity of the music items consumed? Subquestions that have to be dealt with:

Supervisor: Christine Bauer
24 July 2024
The influence of music recommender systems on the diversity of music consumed