Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5872))

  • 1295 Accesses

Abstract

Recommender Systems have become a significant area in the context of web personalization, given the large amount of available data. Ontologies can be widely taken advantage of in recommender systems, since they provide a means of classifying and discovering of new information about the items to recommend, about user profiles and even about their context. We have developed a semantically enhanced recommender system based on this kind of ontologies. In this paper we present a description of the proposed system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. Middleton, S.E., Shadbolt, N., De Roure, D.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004)

    Article  Google Scholar 

  4. Wang, R.-Q., Kong, F.-S.: Semantic-Enhanced Personalized Recommender System. In: International Conference on Machine Learning and Cybernetics, vol. 7, pp. 4069–4074 (2007)

    Google Scholar 

  5. Liu, P., Nie, G., Chen, D.: Exploiting Semantic Descriptions of Products and User Profiles for Recommender Systems. Computational Intelligence and Data Mining, 179–185 (2007)

    Google Scholar 

  6. Ziegler, C., Schmidt-Thieme, L., Lausen, G.: Exploiting semantic product descriptions for recommender systems. In: Proc. 2nd ACMSIGIR SemanticWeb and IR WS (2004)

    Google Scholar 

  7. Moshfeghi, Y., Agarwal, D., Piwowarski, B., Jose, J.M.: Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering. In: Boughanem, M., et al. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 54–65. Springer, Heidelberg (2009)

    Google Scholar 

  8. Linden, G., Smith, B., York, J.: Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Distributed Systems Online 4(1) (2003)

    Google Scholar 

  9. Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Sys. 22(1), 143–177 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ruiz-Montiel, M., Aldana-Montes, J.F. (2009). Semantically Enhanced Recommender Systems. In: Meersman, R., Herrero, P., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2009 Workshops. OTM 2009. Lecture Notes in Computer Science, vol 5872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05290-3_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-05290-3_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05289-7

  • Online ISBN: 978-3-642-05290-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics