Linked Open Data has been recognized as a useful source of background knowledge for building content-based recommender sys- tems. While many existing approaches transform that data into a propo- sitional form, we investigate how the graph nature of Linked Open Data can be exploited when building recommender systems. In particular, we use path lengths, the K-Step Markov approach, as well as weighted NI paths to compute item relevance and perform a content-based recom- mendation. An evaluation on the three tasks of the 2015 LOD-RecSys challenge shows that the results are promising, and, for cross-domain recommendations, outperform collaborative filtering.
['Petar Ristoski', 'Michael Schuhmacher', 'Heiko Paulheim']
Using Graph Metrics for Linked Open Data Enabled Recommender Systems