Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular.

In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score.

The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages.

推荐系统是采用高级算法的决策支持工具，以帮助用户找到他们可能感兴趣的较少探索的项目。虽然推荐系统可能提供一系列吸引人的好处，但它们也可能加剧不良影响，例如受欢迎程度偏差，其中一些受欢迎的用户/项目变得更受欢迎，而许多不受欢迎的用户/项目变得更不受欢迎。

在本文中，我们研究了不同推荐算法在不同应用领域和推荐场景中对流行度偏差的影响。我们通过考虑两种不同的推荐场景设计了综合评估方法，即基于用户的场景（例如，向用户推荐用户关注）和基于项目的场景（例如，向用户推荐项目以消费）。我们使用了两个大型数据集 Twitter 和 Movielens，并通过考虑各种指标（例如 PR-AUC、RCE、基尼指数和熵分数）来比较各种经典和现代推荐算法。

结果表明，不同场景和不同推荐领域之间存在显着差异。根据我们的观察，虽然用户向用户的推荐可能会增加系统中的人气偏差，但商品向用户的推荐确实可能会减少它。此外，虽然我们在不同的语言（即英语、西班牙语、葡萄牙语和日语）中测量了不同程度的流行偏见，但上述现象在所有这些语言中始终如一地观察到。