Adaptive Utility-Based Recommendation
Knowledge-based recommenders support customers in preference construction processes related to complex products and services. In this context, utility constraints (scoring rules) play an important role. They determine the order in which items (products and services) are presented to customers. In many cases utility constraints are faulty, i.e., calculate rankings which are not expected and accepted by marketing and sales experts. The adaptation of these constraints is extremely time-consuming and often an error-prone process. In this paper we present an approach which effectively supports the automated adaptation of utility constraint sets based on solutions for corresponding nonlinear optimization problems. This approach significantly increases the applicability of knowledge-based recommendation by allowing the automated reproduction of item rankings specified by marketing and sales experts.
KeywordsUtility-based recommendation non-linear optimization
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- 1.Burke, R.: Knowledge-based Recommender Systems. Encyclopedia of Library and Information Systems 69(32), 180–200 (2000)Google Scholar
- 2.Biso, A., Rossi, F., Sperduti, A.: Experimental Results on Learning Soft Constraints. In: KR02, pp. 435–444 (2000)Google Scholar
- 3.Carenini, G., Moore, J.: Generating and evaluating evaluative arguments. AI Journal 170, 925–952 (2006)Google Scholar
- 7.Felfernig, A., Friedrich, G., Teppan, E., Isak, K.: Intelligent Debugging and Repair of Utility Constraint Sets in Knowledge-based Recommender Applications. In: 13th ACM Intl. IUI Conf., Canary Islands, Spain, January 13-16 (2008)Google Scholar
- 8.Felfernig, A., Isak, K., Szabo, K., Zachar, P.: The VITA Financial Services Sales Support Environment. In: AAAI/IAAI 2007, Canada, pp. 1692–1699 (2007)Google Scholar
- 9.Felfernig, A., Friedrich, G., Gula, B., Hitz, M., Kruggel, T., Melcher, R., Riepan, D., Strauss, S., Teppan, E., Vitouch, O.: Persuasive Recommendation: Exploring Serial Position Effects in Knowledge-based Recommender Systems. In: de Kort, Y.A.W., IJsselsteijn, W.A., Midden, C., Eggen, B., Fogg, B.J. (eds.) PERSUASIVE 2007. LNCS, vol. 4744, pp. 283–294. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 10.Fourer, R., Gay, D., Kernighan, B.: AMPL: A Modeling Language for Mathematical Programming. Cole Publishing Company (2002)Google Scholar
- 12.Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. John Wiley and Sons, Chichester (1976)Google Scholar
- 14.Winterfeldt, D., Edwards, W.: Decision Analysis and Behavioral Research. Cambridge University Press, Cambridge (1986)Google Scholar