Handling conditional preferences in recommender systems

ABSTRACT

In this paper, we propose an approach to handle conditional preferences in recommender systems. A quantitative conditional preference model based on domain knowledge is introduced. The inheritance property in concept trees and bipolar property in preference statements are adopted when interpreting conditional preference rules. Group preferences are merged from personal preferences with consideration of manipulability. A graphical user interface is developed for visualization of domain knowledge, conditional preference rules, personal and group preferences.

REFERENCES

• 1: Keeney, R.L., and Raiffa, H. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley series in probability and mathematical statistics, John Wiley & Sons Inc. (1976).
• 2: Pat Langley, Elements of machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1995
• 3: Salem Benferhat , Didier Dubois , Souhila Kaci , Henri Prade, Bipolar possibility theory in preference modeling: Representation, fusion and optimal solutions, Information Fusion, v.7 n.1, p.135-150, March, 2006 [doi>10.1016/j.inffus.2005.04.001]
• 4: Chen, A. Context-Aware Collaborative Filtering System: Predicting the User's Preference in the Ubiquitous Computing Environment. In Proc. of LoCA (2005), 244--253.
• 5: Stefanidis, K., Pitoura, E., and Vassiliadis, P. Modeling and Storing Context-Aware Preferences. In Proc. of ADBIS (2006), 124--140.
• 6: Boutilier, C., Brafman, R.I., Hoos, H.H., and Poole, D. Reasoning with Conditional ceteris paribus Preference Statements. In Proc. of UAI'99, Morgan Kaufmann Publishers Inc. (1999), 71--80.
• 7: Wilson, N. Extending CP-nets with Stronger Conditional Preference Statements. In Proc. of the 19th National Conference on Artificial Intelligence, AAAI (2004), 735--741.
• 8: Domshlak, C., Rossi, F., Venable, K.B., and Walsh, T. Reasoning about Soft Constraints and Conditional Preferences: Complexity Results and Approximation Techniques. In Proc. of IJCAI'03, Morgan Kaufmann Publishers Inc. (2003), 215--220.
• 9: Anthony Jameson, More than the sum of its members: challenges for group recommender systems, Proceedings of the working conference on Advanced visual interfaces, May 25-28, 2004, Gallipoli, Italy [doi>10.1145/989863.989869]
• 10: Andrew Crossen , Jay Budzik , Kristian J. Hammond, Flytrap: intelligent group music recommendation, Proceedings of the 7th international conference on Intelligent user interfaces, January 13-16, 2002, San Francisco, California, USA [doi>10.1145/502716.502748]
• 11: Yu, Z., Yu, Z., Zhou, X., and Nakamura, Y. Meeting Warming-up: Detecting Common Interests and Conflicts before a Meeting. In Adjunct Proc. of CSCW (2008).
• 12: Schickel-Zuber, V., and Faltings, B. Heterogeneous Attribute Utility Model: A New Approach for Modeling User Profiles for Recommendation Systems. In Proc. of the WebKDD'05 Workshop at KDD'05, ACM Press (2005), 80--91.
• 13: Zhiwen Yu , Xingshe Zhou , Yanbin Hao , Jianhua Gu, TV Program Recommendation for Multiple Viewers Based on user Profile Merging, User Modeling and User-Adapted Interaction, v.16 n.1, p.63-82, March 2006 [doi>10.1007/s11257-006-9005-6]
• 14: Jameson, A., and Smyth, B. Recommendation to Groups. The Adaptive Web, LNCS 4321 (2007), 596--627.
• 15: Jameson, A., Baldes, S., and Kleinbauer, T. Enhancing Mutual Awareness in Group Recommender Systems. In Proc. of the IJCAI'03 Workshop on Intelligent Techniques for Web Personalization, AAAI (2003).

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.3 Group and Organization Interfaces
          Subjects: Computer-supported cooperative work

General Terms:
Design, Human Factors

Keywords:
conditional preference, recommender system