Automatic Optimization of Web Recommendations Using Feedback and Ontology Graphs

  • Nick Golovin
  • Erhard Rahm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3579)


Web recommendation systems have become a popular means to improve the usability of web sites. This paper describes the architecture of a rule-based recommendation system and presents its evaluation on two real-life applications. The architecture combines recommendations from different algorithms in a recommendation database and applies feedback-based machine learning to optimize the selection of the presented recommendations. The recommendations database also stores ontology graphs, which are used to semantically enrich the recommendations. We describe the general architecture of the system and the test setting, illustrate the application of several optimization approaches and present comparative results.


  1. 1.
    Acharyya, S., Ghosh, J.: Context-Sensitive Modeling of Web-Surfing Behavior using Concept Trees. In: Proc. WebKDD (2003)Google Scholar
  2. 2.
    Balabanovic, M.: An Adaptive Web Page Recommendation Service. CACM (1997)Google Scholar
  3. 3.
    Basilico, J., Hofmann, T.: Unifying collaborative and content-based filtering. In: Proc. 21th ICML Conference, Banff, Canada (2004)Google Scholar
  4. 4.
    Baron, S., Spiliopoulou, M.: Monitoring the Evolution of Web Usage Patterns. In: Proc. ECML/PKDD (2003)Google Scholar
  5. 5.
    Burke, R.: Hybrid Recommender Systems: Survey and Experiments. In: User Modeling and User-Adapted Interaction (2002)Google Scholar
  6. 6.
    Claypool, M., Gokhale, A., Miranda, T.: Combining Content-Based and Collaborative Filters in an Online Newspaper. In: Proc. ACM SIGIR Workshop on Recommender Systems (1999)Google Scholar
  7. 7.
    Golovin, N., Rahm, E.: Reinforcement Learning Architecture for Web Recommendations. In: Proc. ITCC 2004. IEEE, Los Alamitos (2004)Google Scholar
  8. 8.
    ten Hagen, S., van Someren, M., Hollink, V.: Exploration/exploitation in adaptive commender systems. In: Proc. European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation in Smart Adaptive Systems, Oulu, Finland (2003)Google Scholar
  9. 9.
    Jameson, A., Konstan, J., Riedl, J.: AI Techniques for Personalized Recommendation. Tutorial presented at AAAI (2002)Google Scholar
  10. 10.
    Linden, G., Smith, B., York, J.: Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing (January 2003)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Nakagawa, M., Mobasher, B.: A Hybrid Web Personalization Model Based on Site Connectivity. In: Proc. 5th WEBKDD workshop, Washington, DC, USA (August 2003)Google Scholar
  13. 13.
    Paulson, P., Tzanavari, A.: Combining Collaborative and Content-Based Filtering Using Conceptual Graphs. In: Lawry, J., G. Shanahan, J., L. Ralescu, A. (eds.) Modelling with Words. LNCS, vol. 2873. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Reategui, E., Campbell, J., Torres, R.: R. Using Item Descriptors in Recommender Systems. In: AAAI Workshop on Semantic Web Personalization, San Jose, USA (2004)Google Scholar
  15. 15.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-Commerce. In: Proc. ACME-Commerce (2000)Google Scholar
  16. 16.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  17. 17.
    Thor, A., Rahm, E.: AWESOME - A Data Warehouse-based System for Adaptive Website Recommendations. In: Proc. 30th Intl. Conf. on Very Large Databases (VLDB), Toronto (August 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Nick Golovin
    • 1
  • Erhard Rahm
    • 1
  1. 1.University of LeipzigLeipzigGermany

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