Abstract
The ultimate mission of a Recommender System (RS) is to help users discover items they might be interested in. In order to be really useful for the end-user, Content-based (CB) RSs need both to harvest as much information as possible about such items and to effectively handle it. The boom of Linked Open Data (LOD) datasets with their huge amount of semantically interrelated data is thus a great opportunity for boosting CB-RSs. In this paper we present a CB-RS that leverages LOD and profits from a neighborhood-based graph kernel. The proposed kernel is able to compute semantic item similarities by matching their local neighborhood graphs. Experimental evaluation on the MovieLens dataset shows that the proposed approach outperforms in terms of accuracy and novelty other competitive approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bellogín, A., Cantador, I., Castells, P.: A study of heterogeneity in recommendations for a social music service. In: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2010, pp. 1–8. ACM Press, New York (2010)
Cantador, I., Bellogín, A., Castells, P.: A multilayer ontology-based hybrid recommendation model. AI Commun. Special Issue on Rec. Sys. 21(2-3), 203–210 (2008)
de Vries, G.K.D.: A fast approximation of the weisfeiler-lehman graph kernel for RDF data. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part I. LNCS (LNAI), vol. 8188, pp. 606–621. Springer, Heidelberg (2013)
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 (2012)
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 (2012)
Gärtner, T., Flach, P.A., Wrobel, S.: On graph kernels: Hardness results and efficient alternatives. In: COLT, pp. 129–143 (2003)
Heitmann, B., Hayes, C.: Using linked data to build open, collaborative recommender systems. In: AAAI Spring Symposium: Linked Data Meets Artificial Intelligence (2010)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Khan, A., Li, N., Yan, X., Guan, Z., Chakraborty, S., Tao, S.: Neighborhood based fast graph search in large networks. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, pp. 901–912. ACM, New York (2011)
Khrouf, H., Troncy, R.: Hybrid event recommendation using linked data and user diversity. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys 2013, pp. 185–192. ACM, New York (2013)
Lösch, U., Bloehdorn, S., Rettinger, A.: Graph kernels for RDF data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 134–148. Springer, Heidelberg (2012)
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: How accuracy metrics have hurt recommender systems. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems, CHI EA 2006, pp. 1097–1101. ACM, New York (2006)
Middleton, S.E., Roure, D.D., Shadbolt, N.R.: Ontology-based recommender systems. In: Handbook on Ontologies, vol. 32(6), pp. 779–796 (2009)
Mobasher, B., Jin, X., Zhou, Y.: Semantically enhanced collaborative filtering on the web. In: Berendt, B., Hotho, A., Mladenič, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds.) EWMF 2003. LNCS (LNAI), vol. 3209, pp. 57–76. Springer, Heidelberg (2004)
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, RecSys 2013, pp. 85–92. ACM, New York (2013)
Ostuni, V.C., Gentile, G., Di Noia, T., Mirizzi, R., Romito, D., Di Sciascio, E.: Mobile movie recommendations with linked data. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 400–415. Springer, Heidelberg (2013)
Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Peska, L., Vojtas, P.: Using linked open data to improve recommending on e-commerce. In: 2nd International Workshop on Semantic Technologies meet Recommender Systems & Big Data (SeRSy 2013). CEUR-WS (2013)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, New York (2004)
Shervashidze, N., Schweitzer, P., van Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-lehman graph kernels. J. Mach. Learn. Res. 12, 2539–2561 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ostuni, V.C., Di Noia, T., Mirizzi, R., Di Sciascio, E. (2014). A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel. In: Hepp, M., Hoffner, Y. (eds) E-Commerce and Web Technologies. EC-Web 2014. Lecture Notes in Business Information Processing, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-319-10491-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-10491-1_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10490-4
Online ISBN: 978-3-319-10491-1
eBook Packages: Computer ScienceComputer Science (R0)