Advertisement

Augmenting LOD-Based Recommender Systems Using Graph Centrality Measures

  • Bart van Rossum
  • Flavius FrasincarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11496)

Abstract

In this paper we investigate the incorporation of graph-based features into LOD path-based recommender systems, an approach that so far has received little attention. More specifically, we propose two normalisation procedures that adjust user-item path counts by the degree centrality of the nodes connecting them. Evaluation on the MovieLens 1M dataset shows that the linear normalisation approach yields a significant increase in recommendation accuracy as compared to the default case, especially in settings where the most popular movies are omitted. These results serve as a fruitful base for further incorporation of graph measures into recommender systems, and might help in establishing the recommendation diversity that has recently gained much attention.

Keywords

Top-N recommendations Linked Open Data Information network schema Random forest 

References

  1. 1.
    Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012)CrossRefGoogle Scholar
  2. 2.
    Altingovde, I.S., Subakan, Ö.N., Ulusoy, Ö.: Cluster searching strategies for collaborative recommendation systems. Inf. Process. Manage. 49(3), 688–697 (2013)CrossRefGoogle Scholar
  3. 3.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC-2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  4. 4.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. Int. J. Semantic Web Inf. Syst. 5(2), 1–22 (2009)CrossRefGoogle Scholar
  5. 5.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  6. 6.
    Chapelle, O., Chang, Y.: Yahoo! Learning to rank challenge overview. In: Proceedings of the Learning to Rank Challenge, pp. 1–24 (2011)Google Scholar
  7. 7.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-N recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46. ACM (2010)Google Scholar
  8. 8.
    Emmons, S., Kobourov, S., Gallant, M., Borner, K.: Analysis of network clustering algorithms and cluster quality metrics at scale. PloS One 11(7), e0159161 (2016). https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0159161CrossRefGoogle Scholar
  9. 9.
    Gong, S.: A collaborative filtering recommendation algorithm based on user clustering and item clustering. J. Soc. Work 5(7), 745–752 (2010)Google Scholar
  10. 10.
    Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. ACM Trans. Intell. Syst. Technol. 5(4), 19:1–19:19 (2015). https://dblp.uni-trier.de/rec/bibtex/journals/tiis/HarperK16Google Scholar
  11. 11.
    Jannach, D., Resnick, P., Tuzhilin, A., Zanker, M.: Recommender systems-beyond matrix completion. Commun. ACM 59(11), 94–102 (2016)CrossRefGoogle Scholar
  12. 12.
    Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Boston (2011).  https://doi.org/10.1007/978-0-387-85820-3_3CrossRefGoogle Scholar
  13. 13.
    Musto, C., Lops, P., de Gemmis, M., Semeraro, G.: Semantics-aware recommender systems exploiting linked open data and graph-based features. Knowl.-Based Syst. 136, 1–14 (2017)CrossRefGoogle Scholar
  14. 14.
    Noia, T.D., Ostuni, V.C., Tomeo, P., Sciascio, E.D.: SPrank: semantic path-based ranking for top-N recommendations using linked open data. ACM Trans. Intell. Syst. Technol. 8(1), 9:1–9:34 (2016)CrossRefGoogle Scholar
  15. 15.
    Noia, T.D., Rosati, J., Tomeo, P., Sciascio, E.D.: Adaptive multi-attribute diversity for recommender systems. Inf. Sci. 382, 234–253 (2017)CrossRefGoogle Scholar
  16. 16.
    Palumbo, E., Rizzo, G., Troncy, R.: Learning user-item relatedness from knowledge graphs for top-N item recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys 2017), pp. 32–36. ACM (2017)Google Scholar
  17. 17.
    Wever, T., Frasincar, F.: A linked open data schema-driven approach for Top-N recommendations. In: Proceedings of the Symposium on Applied Computing (SAC 2017), pp. 656–663. ACM (2017)Google Scholar
  18. 18.
    Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys 2008), pp. 123–130. ACM (2008)Google Scholar
  19. 19.
    Zhou, T., Kuscsik, Z., Liu, J.G., Medo, M., Wakeling, J.R., Zhang, Y.C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Nat. Acad. Sci. 107(10), 4511–4515 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

Personalised recommendations