ANYTIME AHP METHOD FOR PREFERENCES ELICITATION IN STEREOTYPE-BASED RECOMMENDER SYSTEM

Lior Rokach, Alon Schclar, Amnon Meisels

2008

Abstract

In stereotype-based recommendation systems, user profiles are represented as an affinity vector of stereotypes. Upon the registration of new users, the system needs to assign the new users to existing stereotypes. The AHP (Analytic Hierarchy Process) methodology can be used for initial elicitation of user preferences. However, using the AHP procedure as-is will require the user to respond to a very long set of pairwise comparison questions. We suggest a novel method for converting AHP into an anytime approach. At each stage, the user may choose not to continue. However, the system is still able to provide some classification into a stereotype. The more answers the user provides, the more specific the classification becomes.

References

  1. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331-370.
  2. Carmone, F. J., Kara, A., and Zanakis, S. H. (1997). A monte carlo investigation of incomplete pairwise comparison matrices in ahp. European Journal of Operational Research, 102(3):538-553.
  3. Dryer, J. S. (1990). Remarks on the analytic hierarchy process. Management science, 36(3):249-258.
  4. Golany, B. and Kress, M. (1993). A multicriteria evaluation of methods for obtaining weights from ratio-scale matrices. European Journal of Operations Research, 69:210-220.
  5. Harker, P. T. (1987). The incomplete pairwise comparisons in the analytic hierarchy process. Mathematical and Computer Modelling, 9(11):837-848.
  6. Liu, D. and Shih, Y. (2005). Integrating ahp and data mining for product recommendation based on customer lifetime value. Information and Management, 42(3):387- 400.
  7. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63:81-97.
  8. Montaner, M., Lopez, B., and Rosa, J. L. D. L. (2003). A taxonomy of recommender agents on the internet. Artificial Intelligence Review, 19:285-330.
  9. Montgomery, D. C. (1997). Design and Analysis of Experiments. 4th edition, Wiley, New York.
  10. Orwant, J. (1995). Heterogeneous learning in the doppelgänger user modeling system. User Model. UserAdapt. Interact., 4(2):107-130.
  11. Pu, P., Faltings, B., and Torrens, M. (2003). User-involved preference elicitation. In workshop notes of the Workshop on Configuration, the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI'03), pages 56-63.
  12. Quinlan, J. R. (1993). C4.5: Programs For Machine Learning. Morgan Kaufmann, Los Altos.
  13. Ray, S. (2004). An AHP based decision model to evaluate various e-learning packages. In 6th International Conference on Enterprise Information Systems.
  14. Resnick, P. and Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3):56-58.
  15. Rich, E. (1979). User modeling via stereotypes. Cognitive Science, 3(4):329-354.
  16. Satty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology.
  17. Satty, T. L. (1980). The Analytic Hierarchy Process. McGraw-Hill International, New York, U.S.A.
  18. Satty, T. L. (2001). The seven pillars of the analytic hierarchy process. In Köksalan, M. and Zionts, S., editors, Multiple Criteria Decision Making in the New Millennium, chapter 2. Springer.
  19. Taguchi, G. and Konishi, S. (1987). Orthogonal Arrays and Linear Graphs. American Supplier Institute Inc., Dearborn, MI.
  20. Viappiani, P., Faltings, B., and Pu, P. (2006). Evaluating preference-based search tools: a tale of two approaches. In Proceedings of the Twenty-first National Conference on Artificial Intelligence (AAAI06), pages 16-20, Boston, USA.
  21. Weiss, E. N. and Rao, V. R. (1987). Ahp design issues for large scale system. Decision Science, 18(1):43-61.
Download


Paper Citation


in Harvard Style

Rokach L., Schclar A. and Meisels A. (2008). ANYTIME AHP METHOD FOR PREFERENCES ELICITATION IN STEREOTYPE-BASED RECOMMENDER SYSTEM . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 268-275. DOI: 10.5220/0001697902680275


in Bibtex Style

@conference{iceis08,
author={Lior Rokach and Alon Schclar and Amnon Meisels},
title={ANYTIME AHP METHOD FOR PREFERENCES ELICITATION IN STEREOTYPE-BASED RECOMMENDER SYSTEM},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2008},
pages={268-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001697902680275},
isbn={978-989-8111-37-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - ANYTIME AHP METHOD FOR PREFERENCES ELICITATION IN STEREOTYPE-BASED RECOMMENDER SYSTEM
SN - 978-989-8111-37-1
AU - Rokach L.
AU - Schclar A.
AU - Meisels A.
PY - 2008
SP - 268
EP - 275
DO - 10.5220/0001697902680275