Factorization Machines Leveraging Lightweight Linked Open Data-Enabled Features for Top-N Recommendations

  • Guangyuan PiaoEmail author
  • John G. Breslin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10570)


With the popularity of Linked Open Data (LOD) and the associated rise in freely accessible knowledge that can be accessed via LOD, exploiting LOD for recommender systems has been widely studied based on various approaches such as graph-based or using different machine learning models with LOD-enabled features. Many of the previous approaches require construction of an additional graph to run graph-based algorithms or to extract path-based features by combining user-item interactions (e.g., likes, dislikes) and background knowledge from LOD. In this paper, we investigate Factorization Machines (FMs) based on particularly lightweight LOD-enabled features which can be directly obtained via a public SPARQL Endpoint without any additional effort to construct a graph. Firstly, we aim to study whether using FM with these lightweight LOD-enabled features can provide competitive performance compared to a learning-to-rank approach leveraging LOD as well as other well-established approaches such as kNN-item and BPRMF. Secondly, we are interested in finding out to what extent each set of LOD-enabled features contributes to the recommendation performance. Experimental evaluation on a standard dataset shows that our proposed approach using FM with lightweight LOD-enabled features provides the best performance compared to other approaches in terms of five evaluation metrics. In addition, the study of the recommendation performance based on different sets of LOD-enabled features indicate that property-object lists and PageRank scores of items are useful for improving the performance, and can provide the best performance through using them together for FM. We observe that subject-property lists of items does not contribute to the recommendation performance but rather decreases the performance.



This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight Centre for Data Analytics).


  1. 1.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: A Nucleus for a Web of Open Data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  2. 2.
    Di Noia, T., Cantador, I., Ostuni, V.C.: Linked Open Data-Enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation. In: Presutti, V., Stankovic, M., Cambria, E., Cantador, I., Di Iorio, A., Di Noia, T., Lange, C., Reforgiato Recupero, D., Tordai, A. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 129–143. Springer, Cham (2014). doi: 10.1007/978-3-319-12024-9_17CrossRefGoogle Scholar
  3. 3.
    Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D.: Exploiting the web of data in model-based recommender systems. In: Proceedings of the 6th ACM Conference on Recommender Systems, pp. 253–256. ACM (2012)Google Scholar
  4. 4.
    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, pp. 1–8. ACM (2012)Google Scholar
  5. 5.
    Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: MyMediaLite: a free recommender system library. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 305–308. ACM, New York (2011)Google Scholar
  6. 6.
    de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-Aware Content-Based Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 119–159. Springer, Boston, MA (2015). doi: 10.1007/978-1-4899-7637-6_4CrossRefGoogle Scholar
  7. 7.
    Haveliwala, T.H.: Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)CrossRefGoogle Scholar
  8. 8.
    Heath, T., Bizer, C.: Linked data: evolving the web into a global data space. In: Synthesis lectures on the semantic web: theory and technology, vol. 1(1), pp. 1–136 (2011)CrossRefGoogle Scholar
  9. 9.
    Heitmann, B.: An open framework for multi-source, cross-domain personalisation with semantic interest graphs. In: Proceedings of the sixth ACM conference on Recommender systems, pp. 313–316. ACM (2012)Google Scholar
  10. 10.
    Heitmann, B., Hayes, C.: Using linked data to build open, collaborative recommender systems. In: AAAI spring symposium: linked data meets artificial intelligence, pp. 76–81 (2010)Google Scholar
  11. 11.
    Heitmann, B., Hayes, C.: SemStim at the LOD-RecSys 2014 Challenge. In: Presutti, V., Stankovic, M., Cambria, E., Cantador, I., Di Iorio, A., Di Noia, T., Lange, C., Reforgiato Recupero, D., Tordai, A. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 170–175. Springer, Cham (2014). doi: 10.1007/978-3-319-12024-9_22CrossRefGoogle Scholar
  12. 12.
    Jeh, G., Widom, J.: SimRank: A measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 538–543. ACM, New York (2002)Google Scholar
  13. 13.
    Koren, Y.: Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 447–456. ACM, New York (2009)Google Scholar
  14. 14.
    Lalithsena, S., Kapanipathi, P., Sheth, A.: Harnessing relationships for domain-specific subgraph extraction: a recommendation use case. In: IEEE International Conference on Big Data, Washington D.C. (2016)Google Scholar
  15. 15.
    Leal, J.P.: Using proximity to compute semantic relatedness in RDF graphs. Comput. Sci. Inf. Syst. 10(4), 1727–1746 (2013)CrossRefGoogle Scholar
  16. 16.
    Lehmann, J., et al.: Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web J. 6(2015), 167–195 (2013)Google Scholar
  17. 17.
    Musto, C., Lops, P., Basile, P., de Gemmis, M., Semeraro, G.: Semantics-aware graph-based recommender systems exploiting linked open data. In: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 229–237. ACM (2016)Google Scholar
  18. 18.
    Musto, C., Narducci, F., Lops, P., De Gemmis, M., Semeraro, G.: ExpLOD: A framework for explaining recommendations based on the linked open data cloud. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, pp. 151–154. ACM, New York (2016)Google Scholar
  19. 19.
    Nguyen, P., Tomeo, P., Di Noia, T., Di Sciascio, E.: An evaluation of SimRank and personalized PageRank to build a recommender system for the web of data. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1477–1482. ACM (2015)Google Scholar
  20. 20.
    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. (TIST) 8(1), 9 (2016)Google Scholar
  21. 21.
    Oliveira, J., Delgado, C., Assaife, A.C.: A recommendation approach for consuming linked open data. Expert Syst. Appl. 72, 407–420 (2017)CrossRefGoogle Scholar
  22. 22.
    Ostuni, V.C., Di Noia, T., Di Sciascio, E., Mirizzi, R.: Top-n Recommendations from Implicit Feedback Leveraging Linked Open Data. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 85–92. ACM (2013)Google Scholar
  23. 23.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report (1999)Google Scholar
  24. 24.
    Passant, A.: dbrec: Music Recommendations Using DBpedia. In: ISWC 2010 SE - 14, pp. 209–224 (2010)Google Scholar
  25. 25.
    Passant, A.: Measuring semantic distance on linking data and using it for resources recommendations. In: AAAI Spring Symposium: Linked Data Meets Artificial Intelligence, vol. 77, p. 123 (2010)Google Scholar
  26. 26.
    Piao, G., Ara, S., Breslin, J.G.: Computing the Semantic Similarity of Resources in DBpedia for Recommendation Purposes. In: Qi, G., Kozaki, K., Pan, J.Z., Yu, S. (eds.) JIST 2015. LNCS, vol. 9544, pp. 185–200. Springer, Cham (2016). doi: 10.1007/978-3-319-31676-5_13CrossRefGoogle Scholar
  27. 27.
    Piao, G., Breslin, J.G.: Measuring semantic distance for linked open data-enabled recommender systems. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 315–320. ACM (2016)Google Scholar
  28. 28.
    Rendle, S.: Factorization machines. In: Data Mining (ICDM), 2010 IEEE 10th International Conference on, pp. 995–1000. IEEE (2010)Google Scholar
  29. 29.
    Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 57:1–57:22 (2012)CrossRefGoogle Scholar
  30. 30.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452–461. AUAI Press, Arlington (2009)Google Scholar
  31. 31.
    Rowe, M.: Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 341–356. Springer, Cham (2014). doi: 10.1007/978-3-319-11964-9_22CrossRefGoogle Scholar
  32. 32.
    Thalhammer, A., Rettinger, A.: PageRank on Wikipedia: Towards General Importance Scores for Entities. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 227–240. Springer, Cham (2016). doi: 10.1007/978-3-319-47602-5_41CrossRefGoogle Scholar
  33. 33.
    Wu, Q., Burges, C.J.C., Svore, K.M., Gao, J.: Adapting boosting for information retrieval measures. Inf. Retrieval 13(3), 254–270 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Insight Centre for Data AnalyticsNational University of Ireland GalwayGalwayIreland

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