Embedding knowledge graphs for semantics-aware recommendations based on DBpedia

In this paper we present a semantics-aware recommendation strategy that uses graph embedding techniques to learn a vector space representation of the items to be recommended. Such a representation relies on the tripartite graph which connects users, items and entities gathered from DBpedia, thus it encodes both collaborative and content-based information. These embeddings are then used to feed with positive and negative examples (the items the user liked and those she did not like) a classification model, which is finally exploited to classify new items as interesting or not interesting for the target user. In the experimental evaluation we evaluate the effectiveness of our method on varying of different graph embedding techniques and on several topologies of the graph. Results show that the embeddings learnt by combining collaborative data points with the information gathered from DBpedia led to the best results and also beat several state-of-the-art techniques