Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

  • Vincent Schickel-Zuber
  • Boi Faltings
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4198)


To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction models. We propose a new method that extends the utility model and assumes that the structure of user preferences follows an ontology of product attributes. Using the data of the MovieLens system, we show experimentally that real user preferences indeed closely follow an ontology based on movie attributes. Furthermore, a recommender based just on a single individual’s preferences and this ontology performs better than collaborative filtering, with the greatest differences when little data about the user is available. This points the way to how proper inductive bias can be used for significantly more powerful recommender systems in the future.


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  1. 1.
    Andreasen, T., Bulskov, H., Knappe, R.: From Ontology over Similarity to Query Evaluation. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) CoopIS 2003, DOA 2003, and ODBASE 2003. LNCS, vol. 2888. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    Blythe, J.: Visual Exploration and Incremental Utility Elicitation. In: 18th national conference on Artificial Intelligence, Canada, pp. 526–532 (2002)Google Scholar
  3. 3.
    Bradley, K., Rafter, R., Smyth, B.: Case-Based User Profiling for Content Personalization. In: Brusilovsky, P., Stock, O., Strapparava, C. (eds.) AH 2000. LNCS, vol. 1892, p. 62. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Claypool, M., Gokhale, A., Miranda, T.: Combining Content-Based and Collaborative Filters in an Online Newspaper. In: ACM SIGIR Workshop on Recommender Systems (1999)Google Scholar
  5. 5.
    Debreu, G.: Topological Methods in Cardinal Utility theory. Mathematical Methods in the Social Siences. Standford University Press, California (1960)Google Scholar
  6. 6.
    Fisher, D.H.: Knowledge Acquisition Via Incremental Conceptual Clustering. Machine Learning 2, 139–172 (1987)Google Scholar
  7. 7.
    Ha, V., Haddawys, P.: Problem-focused incremental elicitation of multi-attribute utility model. In: 13th Conf. UAI 1997, pp. 215–222 (1997)Google Scholar
  8. 8.
    Ha, V., Haddawys, P.: A Hybrid Approach to Reasoning with Partially Elicited Preference Models. In: 15th Conf. UAI 1999, pp. 263–270 (1999)Google Scholar
  9. 9.
    Herlocker, J.L., Konstan, J.A., Terven, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  10. 10.
    Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preference and Value Tradeoffs. Cambridge University Press, Cambridge (1993)Google Scholar
  11. 11.
    Linden, G., Smith, B., York, J.: Item-to-Item Collaborative Filtering. IEEE Internet Computing (2003)Google Scholar
  12. 12.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-Boosted Collaborative Filtering. In: ACM SIGIR Workshop on Recommender Systems (2001)Google Scholar
  13. 13.
    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
  14. 14.
    Nasraoui, O., Frigui, H., Joshi, A., Krishnapuram, R.: Mining Web Access Logs Using Relational Competitive Fuzzy Clustering. In: Proc. of the Eighth Int. Fuzzy Systems Association Congress, Hsinchu, Taiwan (1999)Google Scholar
  15. 15.
    Nasraoui, O., Frigui, H., Krishnapuram, R., Joshi, A.: Extracting Web User Profiles Using Relational Competitive Fuzzy Clustering. International Journal on Artificial Intelligence Tools 9(4), 509–526 (2000)CrossRefGoogle Scholar
  16. 16.
    Resnik, P.: Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language. J. of Artificial Intelligence Research, 95–130 (1999)Google Scholar
  17. 17.
    Salzberg, S.L.: On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery 1(3), 317–327 (1997)CrossRefGoogle Scholar
  18. 18.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-Commerce. In: ACM Conf. EC 2000 (2000)Google Scholar
  19. 19.
    Schein, A.L., Popesucl, A., Ungar, L.H., Pennock, D.M.: Methods and Metrics for Cold-Start Recommendations. In: 25th Int. ACM SIGIR 2002 (2002)Google Scholar
  20. 20.
    Stolze, M.: Soft navigation in electronic product catalogs. Int. J. on Digit. Libr. 3(1) (2000)Google Scholar
  21. 21.
    Sullivan, D.O., Smyth, B., Wilson, D.C., McDonald, K., Smeaton, A.: Improving the Quality of the Personalized Electronic Program Guide. User Modeling and User-Adapted Interaction 14(1) (2004)Google Scholar
  22. 22.
    Villard, G.: Computation of the Inverse and Determinant of a Matrix. Algorithms Seminar INRIA, pp. 29–32 (2003)Google Scholar
  23. 23.
    Von Neumann, J., Morgenstern, O.: The Theory of Games and Economic Behavior. Princeton University Press, Princeton (1944)Google Scholar
  24. 24.
    Yang, J., Wenyin, L., Zhang, H., Zhuang, Y.: Thesaurus-Aided Approach For Image Browsing and Retrieval. In: IEEE Int. Conf. on Multimedia and Expo (2001)Google Scholar
  25. 25.
    Zhang, J., Pu, P.: Effort and Accuracy Analysis of Choice Strategies for Electronic Product Catalogs. In: ACM Sym. Applied Computing, pp. 808–814 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vincent Schickel-Zuber
    • 1
  • Boi Faltings
    • 1
  1. 1.School of Computer and Communication Sciences – ICSwiss Federal Institute of Technology (EPFL)LausanneSwitzerland

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