Leveraging Linked Data Analysis for Semantic Recommender Systems

  • Andreas Thalhammer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)


Traditional (Web) link analysis focuses on statistical analysis of links in order to identify “influencial” or “authorative” Web pages like it is done in PageRank, HITS and their variants [10]. Although these techniques are still considered as the backbone of many search engines, the analysis of usage data has gained high importance during recent years [12]. With the arrival of linked data (LD), in particular Linked Open Data (LOD), new information relating to what actually connects different vertices is available. This information can be leveraged in order to develop new techniques that efficiently combine linked data analysis with personalization for identifying not only relevant, but also diverse and even missing information.


Recommender System Link Open Data Recommendation Approach Main Page Music Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Cantador, I., Bellogín, A., Castells, P.: Ontology-based personalised and context-aware recommendations of news items. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2008, vol. 1, pp. 562–565. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  3. 3.
    Cantador, I., Brusilovsky, P., Kuflik, T.: 2nd ws. on information heterogeneity and fusion in recommender systems (hetrec 2011). In: Proc. of the 5th ACM Conf. on Recommender Systems, RecSys 2011. ACM, New York (2011)Google Scholar
  4. 4.
    Cosley, D., Frankowski, D., Terveen, L., Riedl, J.: SuggestBot: Using Intelligent Task Routing to Help People Find Work in Wikipedia. In: Human-Computer Interaction (2007)Google Scholar
  5. 5.
    Dengel, A.: Semantische suche. In: Dengel, A. (ed.) Semantische Technologien, pp. 231–256. Spektrum Akademischer Verlag (2012)Google Scholar
  6. 6.
    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 (2011)Google Scholar
  7. 7.
    Heitmann, B., Hayes, C.: Using Linked Data to Build Open, Collaborative Recommender Systems. Artificial Intelligence (2010)Google Scholar
  8. 8.
    Huang, E., Kim, H.J.: Task Recommendation on Wikipedia. Data Processing (2010)Google Scholar
  9. 9.
    Murakami, T., Mori, K., Orihara, R.: Metrics for Evaluating the Serendipity of Recommendation Lists. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 40–46. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Ng, A.Y., Zheng, A.X., Jordan, M.I.: Stable Algorithms for Link Analysis. Machine Learning, 267–275 (2001)Google Scholar
  11. 11.
    Oren, E., Gerke, S., Decker, S.: Simple Algorithms for Predicate Suggestions Using Similarity and Co-occurrence. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 160–174. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Pariser, E.: The filter bubble: what the Internet is hiding from you. Viking, London (2011)Google Scholar
  13. 13.
    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
  14. 14.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 285–295. ACM, New York (2001)CrossRefGoogle Scholar
  15. 15.
    Wang, Y.: Semantically-Enhanced Recommendations in Cultural Heritage. PhD thesis, Technische Universiteit Eindhoven (2011)Google Scholar
  16. 16.
    Wikipedia. Modelling wikipedia’s growth,'s_growth (online accessed March 12, 2012)
  17. 17.
    Zhang, H., Fu, L., Wang, H., Zhu, H., Wang, Y., Yu, Y.: Eachwiki: Suggest to be an easy-to-edit wiki interface for everyone. In: Semantic Web Challenge (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andreas Thalhammer
    • 1
  1. 1.STI InnsbruckUniversity of InnsbruckAustria

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