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In the past years, knowledge graphs have proven to be beneficial for recommender systems, efficiently addressing paramount issues such as new items and data sparsity. Graph embeddings algorithms have shown to be able to automatically learn high quality feature vectors from graph structures, enabling vector-based measures of node relatedness. In this paper, we show how node2vec can be used to generate item recommendations by learning knowledge graph embeddings. We apply node2vec on a knowledge graph built from the MovieLens 1M dataset and DBpedia and use the node relatedness to generate item recommendations. The results show that node2vec consistently outperforms a set of collaborative filtering baselines on an array of relevant metrics.