  Several studies have identified discrepancies between the popularity of items
in user profiles and the corresponding recommendation lists. Such behavior,
which concerns a variety of recommendation algorithms, is referred to as
popularity bias. Existing work predominantly adopts simple statistical
measures, such as the difference of mean or median popularity, to quantify
popularity bias. Moreover, it does so irrespective of user characteristics
other than the inclination to popular content. In this work, in contrast, we
propose to 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). This
results in a more detailed picture of the characteristics of popularity bias.
Furthermore, we investigate whether such algorithmic popularity bias affects
users of different genders in the same way. We focus on music recommendation
and conduct experiments on the recently released standardized LFM-2b dataset,
containing listening profiles of Last.fm users. We investigate the algorithmic
popularity bias of seven common recommendation algorithms (five collaborative
filtering and two baselines). Our experiments show that (1) the studied metrics
provide novel insights into popularity bias in comparison with only using
average differences, (2) algorithms less inclined towards popularity bias
amplification do not necessarily perform worse in terms of utility (NDCG), (3)
the majority of the investigated recommenders intensify the popularity bias of
the female users.
