CARD: A decision-guidance framework and application for recommending composite alternatives

RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems

Volumn , Issue , 2008, Pages 171-178

  Brodsky, Alex ^a   Henshaw, Sylvia Morgan ^a   Whittle, Jon ^b  

^a GEORGE MASON UNIVERSITY   (United States)

^b LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Decision guidance; Decision optimization; Development framework; Preference learning; Ranking; Recommender systems

Indexed keywords

DECISION GUIDANCE; DECISION OPTIMIZATION; DEVELOPMENT FRAMEWORK; PREFERENCE LEARNING; RANKING; RECOMMENDER SYSTEMS;

KNOWLEDGE BASED SYSTEMS; OPTIMIZATION;

EDUCATION;

EID: 63449122615     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1454008.1454037     Document Type: Conference Paper

References (15)

1. Adomavicius, G. and A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 2005. 17(6): p. 734-749.
2. Bradley, K. and B. Smyth, Improving Recommendation Diversity. Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland, 2001: p. 85-94.
3. Brodsky, A. and X.S. Wang, Decision-Guidance Management Systems (DGMS): Seamless Integration of Data Acquisition, Learning, Prediction and Optimization. Hawaii International Conference on System Sciences, Proceedings of the 41st Annual, 2008: p. 71-71.
4. Burke, R., Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 2002. 12(4): p. 331-370.
5. Konstan, J.A., et al., GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM, 1997. 40(3): p. 77-87.
6. Linden, G., S. Hanks, and N. Lesh, Interactive assessment of user preference models: The automated travel assistant. Proceedings of the Sixth International Conference on User Modeling, 1997. 6778.
7. Manouselis, N. and C. Costopoulou, Analysis and Classification of Multi-Criteria Recommender Systems. World Wide Web, 2007. 10(4): p. 415-441.
8. McGinty, L. and B. Smyth, Comparison-Based Recommendation. Proceedings of the Sixth European Conference on Case-Based Reasoning (ECCBR-02), 2002: p. 575-589.
9. Pazzani, M. and D. Billsus, Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning, 1997. 27(3): p. 313-331.
10. Ricci, F., et al., ITR: a case-based travel advisory system. 6th European Conference on Case Based Reasoning, ECCBR, 2002: p. 613-627.
11. Smyth, B. and P. McClave, Similarity vs. Diversity. Case-Based Reasoning Research and Development: 4th International Conference on Case-Based Reasoning, ICCBR 2001, Vancouver, BC, Canada, July 30-August 2, 2001: Proceedings, 2001.
12. C. Domshlak, F. Rossi, B. Venable, and T. Walsh, Reasoning about Soft Constraints and Conditional Preferences, IJCAI-03
13. Armin Stahl, Ralph Bergmann: Applying Recursive CBR for the Custumization of Structured Products in an Electronic Shop. EWCBR 2000: 297-308
14. David McSherry: Diversity-Conscious Retrieval. ECCBR 2002:219-233
15. G. Adomavicius and Y. Kwon, New Recommendation Techniques for Multi-Criteria Rating Systems. IEEE Intelligent Systems, vol. 22, no. 3, May/June 2007