Metrics

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
Metrics to measure the fairness of music recommender approaches from the perspective of artists

Idea

What metrics help measure the fairness of music recommender approaches from the perspective of artists (item providers)? There is literature on fairness metrics, frequently targeting the user perspective. What about the item provider’s perspective?

Supervisor: Christine Bauer
24 July 2024
Metrics to measure the fairness of music recommender approaches from the perspective of artists