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.
翻译:DBpedia 和 Wikigata 等公共知识图被公认为建立基于内容的建议系统的背景知识的有趣来源。 它们可以用来增加关于建议项目和两者间联系的信息。 虽然提出了相当几种利用知识图的方法,但大多数目的是在使用固定知识图的同时优化建议战略。 在本文中,我们采取不同的方法,即我们修改建议战略,在使用不同基本知识图时观察变化。特别是,我们使用不同的DBpedia 语言版。我们表明,不同知识图的使用不仅导致偏差的推荐系统,而且建议特定建议领域业绩不同的系统。