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 metric