Popularity bias is a well-known issue in recommender systems where few popular items are over-represented in the input data, while majority of other less popular items are under-represented. This disparate representation often leads to bias in exposure given to the items in the recommendation results. Extensive research examined this bias from item perspective and attempted to mitigate it by enhancing the recommendation of less popular items. However, a recent research has revealed the impact of this bias on users. Users with different degree of tolerance toward popular items are not fairly served by the recommendation system: users interested in less popular items receive more popular items in their recommendations, while users interested in popular items are recommended what they want. This is mainly due to the popularity bias that popular items are over-recommended. In this paper, we aim at investigating the factors leading to this user-side unfairness of popularity bias in recommender systems. In particular, we investigate two factors: 1) the relationship between this unfairness and users' interest toward items' categories (e.g., movie genres), 2) the relationship between this unfairness and the diversity of the popularity group in users' profile (the degree to which the user is interested in items with different degree of popularity). Experiments on a movie recommendation dataset using multiple recommendation algorithms show that these two factors are significantly correlated with the degree of popularity unfairness in the recommendation results.

翻译：流行度偏差是推荐系统中的一个众所周知的问题，即少数热门物品在输入数据中过度呈现，而大多数其他不太热门的物品则代表性不足。这种差异化的呈现往往导致推荐结果中物品获得曝光的不公平。大量研究从物品角度审视了这一偏差，并试图通过增强对非热门物品的推荐来缓解它。然而，近期研究揭示了这种偏差对用户的影响。对热门物品容忍度不同的用户并未得到推荐系统的公平服务：对非热门物品感兴趣的用户会收到更多热门物品的推荐，而对热门物品感兴趣的用户则能获得他们想要的推荐。这主要是由于流行度偏差导致热门物品被过度推荐。本文旨在探究推荐系统中导致这种用户侧流行度不公平的因素。具体而言，我们研究了两个因素：1）这种不公平与用户对物品类别（例如电影类型）兴趣之间的关系；2）这种不公平与用户画像中流行度群体多样性（用户对不同流行度物品感兴趣的程度）之间的关系。在电影推荐数据集上使用多种推荐算法进行的实验表明，这两个因素与推荐结果中流行度不公平的程度显著相关。