Public knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems. They can be used to add information about the items to be recommended and links between those. While quite a few approaches for exploiting knowledge graphs have been proposed, most of them aim at optimizing the recommendation strategy while using a fixed knowledge graph. In this paper, we take a different approach, i.e., we fix the recommendation strategy and observe changes when using different underlying knowledge graphs. Particularly, we use different language editions of DBpedia. We show that the usage of different knowledge graphs does not only lead to differently biased recommender systems, but also to recommender systems that differ in performance for particular fields of recommendations.

中文翻译：

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知识图中的偏差-电影推荐和DBpedia不同语言版本的实证研究

诸如DBpedia和Wikidata之类的公共知识图已被认为是构建基于内容的推荐系统的背景知识的有趣来源。它们可用于添加有关要推荐的项目以及这些项目之间的链接的信息。尽管已经提出了许多利用知识图的方法，但大多数方法旨在在使用固定知识图的同时优化推荐策略。在本文中，我们采用了不同的方法，即，我们确定了推荐策略，并在使用不同的基础知识图时观察了变化。特别是，我们使用不同语言版本的DBpedia。我们证明，使用不同的知识图不仅会导致不同的推荐系统，