Abstract
Linked Open Data has been recognized as a useful source of background knowledge for building content-based recommender systems. While many existing approaches transform that data into a propositional 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 recommendation. 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.
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To compute edge weights from IDF, we first normalize the IDF scores to \(\left[ 0;1\right] \), and then assign \(1-IDF_{normalized}\) as a weight to the edges, so that edges with a larger IDF value have a lower weight.
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Acknowledgements
The work presented in this paper has been partly funded by the German Research Foundation (DFG) under grant number PA 2373/1-1 (Mine@LOD). Part of this work was performed on the computational resource bwUniCluster funded by the Ministry of Science, Research and the Arts Baden-Württemberg and the Universities of the State of Baden-Württemberg, Germany, within the framework program bwHPC. We would like to thank our colleague Robert Meusel for his valuable contribution to our system.
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Ristoski, P., Schuhmacher, M., Paulheim, H. (2015). Using Graph Metrics for Linked Open Data Enabled Recommender Systems. In: Stuckenschmidt, H., Jannach, D. (eds) E-Commerce and Web Technologies. EC-Web 2015. Lecture Notes in Business Information Processing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-27729-5_3
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