Advertisement

Using Graph Metrics for Linked Open Data Enabled Recommender Systems

  • Petar RistoskiEmail author
  • Michael Schuhmacher
  • Heiko Paulheim
Conference paper
First Online:
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 239)

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.

Keywords

Linked Open Data Recommender systems Graph metrics Cross-domain recommendation 
This is a preview of subscription content, log in to check access.

Notes

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.

References

  1. 1.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Model. User-Adapted Interact. 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  2. 2.
    Cantador, I., Fernández-Tobıas, I., Berkovsky, S., Cremonesi, P.: Cross-domain recommender systems (2015)Google Scholar
  3. 3.
    Allan, M.: Collins and Elizabeth F Loftus. A spreading-activation theory of semantic processing. Psychol. Rev. 82(6), 407 (1975)CrossRefGoogle Scholar
  4. 4.
    de Borda, J.C.: Mémoire sur les élections au scrutin. Histoire de l’Academie Royale des Sciences (1781)Google Scholar
  5. 5.
    Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D.: Exploiting the web of data in model-based recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 253–256. ACM, New York, NY, USA (2012)Google Scholar
  6. 6.
    Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems, I-SEMANTICS 2012, pp. 1–8. ACM, New York, NY, USA (2012)Google Scholar
  7. 7.
    Fernández-Tobías, I., Cantador, I., Kaminskas, M., Ricci, F.: A generic semantic-based framework for cross-domain recommendation. In: Proceedings of the 2Nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2011, pp. 25–32. ACM, New York, NY, USA (2011)Google Scholar
  8. 8.
    Heitmann, B., Dabrowski, M., Passant, A., Hayes, C., Griffin, K.: Personalisation of social web services in the enterprise using spreading activation for multi-source, cross-domain recommendations. In: AAAI Spring Symposium: Intelligent Web Services Meet Social Computing (2012)Google Scholar
  9. 9.
    Heitmann, B., Conor Hayes, C.: Using linked data to build open, collaborative recommender systems. In: AAAI Spring Symposium: Linked Data Meets Artificial Intelligence (2010)Google Scholar
  10. 10.
    Heitmann, B., Hayes, C.: SemStim at the LOD-RecSys 2014 challenge. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 170–175. Springer, Heidelberg (2014) Google Scholar
  11. 11.
    Kaminskas, M., Fernández-Tobıas, I., Ricci, F., Cantador, I.: Knowledge-based identification of music suited for places of interest. Inf. Technol. Tourism 14(1), 73–95 (2014)CrossRefGoogle Scholar
  12. 12.
    Kaminskas, M., Fernández-Tobías, I., Cantador, I., Ricci, F.: Ontology-based identification of music for places. In: Cantoni, L., (Phil) Xiang, Z. (eds.), Information and Communication Technologies in Tourism 2013, pp. 436–447. Springer, Heidelberg (2013)Google Scholar
  13. 13.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Seman. Web J. (2013)Google Scholar
  14. 14.
    Ostuni, V.C., Di Noia, T., Mirizzi, R., Di Sciascio, E.: A linked data recommender system using a neighborhood-based graph kernel. In: Hepp, M., Hoffner, Y. (eds.) EC-Web 2014. LNBIP, vol. 188, pp. 89–100. Springer, Heidelberg (2014) Google Scholar
  15. 15.
    Ostuni, V.C., Di Noia, T., Mirizzi, R., Di Sciascio, E.: Top-n recommendations from implicit feedback leveraging linked open data. In: IIR, pp. 20–27 (2014)Google Scholar
  16. 16.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical Report 1999–66, Stanford InfoLab, November 1999. Previous number = SIDL-WP-1999-0120Google Scholar
  17. 17.
    Passant, A.: dbrec — Music recommendations using DBpedia. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part II. LNCS, vol. 6497, pp. 209–224. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  18. 18.
    Paulheim, H., Fürnkranz, J.: Unsupervised generation of data mining features from linked open data. In: International Conference on Web Intelligence, Mining, and Semantics (WIMS 2012) (2012)Google Scholar
  19. 19.
    Paulheim, H., Ristoski, P., Mitichkin, E., Bizer, C.: Data mining with background knowledge from the web. In: RapidMiner World (2014)Google Scholar
  20. 20.
    Ristoski, P., Bizer, C., Paulheim, H.: Mining the web of linked data with rapidminer. J. Web Seman. (2015). To appearGoogle Scholar
  21. 21.
    Ristoski, P., Loza Mencía, E., Paulheim, H.: A hybrid multi-strategy recommender system using linked open data. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 150–156. Springer, Heidelberg (2014) Google Scholar
  22. 22.
    Ristoski, P., Paulheim, H.: A comparison of propositionalization strategies for creating features from linked open data. In: Linked Data for Knowledge Discovery (2014)Google Scholar
  23. 23.
    Schmachtenberg, M., Bizer, C., Paulheim, H.: Adoption of the linked data best practices in different topical domains. In: Mika, P., et al. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 245–260. Springer, Heidelberg (2014) Google Scholar
  24. 24.
    Schmachtenberg, M., Strufe, T., Paulheim, H.: Enhancing a location-based recommendation system by enrichment with structured data from the web. In: Web Intelligence, Mining and Semantics (2014)Google Scholar
  25. 25.
    Schuhmacher, M., Ponzetto, S.P.: Knowledge-based graph document modeling. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM 2014, pp. 543–552. ACM, New York, NY, USA (2014)Google Scholar
  26. 26.
    Andreas Thalhammer. Dbpedia pagerank dataset. Downloaded from (2014). http://people.aifb.kit.edu/ath/#DBpedia_PageRank
  27. 27.
    Ting, K.M., Witten, I.H.: Issues in stacked generalization. Artif. Intell. Res. 10(1), 271–289 (1999)zbMATHGoogle Scholar
  28. 28.
    White, S., Smyth, P.: Algorithms for estimating relative importance in networks. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 266–275. ACM, New York, NY, USA (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Petar Ristoski
    • 1
    Email author
  • Michael Schuhmacher
    • 1
  • Heiko Paulheim
    • 1
  1. 1.Research Group Data and Web ScienceUniversity of MannheimMannheimGermany

Personalised recommendations

Cite paper

Buy options