Alessandro B. Melchiorre

TL;DR: A notion of fairness based on the performance gap of a RS between the users with different demographics is defined, and a variety of collaborative filtering algorithms are evaluated in terms of accuracy and beyond-accuracy metrics to explore the fairness in the RS results toward a specific gender group.

TL;DR: This work focuses on the music domain and creates a dataset of Twitter users’ music consumption behavior and personality traits, measuring the latter in terms of the OCEAN model and finds several significant differences in performance between user groups scoring high vs. groups scoring low on several personality traits.

TL;DR: In this paper, the authors investigate popularity differences between the user profile and recommendation list in terms of median, a variety of statistical moments, as well as similarity measures that consider the entire popularity distributions (Kullback-Leibler divergence and Kendall's tau rank-order correlation).

TL;DR: In this paper, the authors investigate popularity differences between the user profile and recommendation list in terms of median, a variety of statistical moments, as well as similarity measures that consider the entire popularity distributions (Kullback-Leibler divergence and Kendall's τ rankorder correlation).

TL;DR: This work identifies several significant medium and weak correlations between music audio features and personality traits, the latter defined by the five-factor model, using audio features of the music users listen to.