Abstract
Preference-based search is the problem of finding an item that matches best with a user’s preferences. User studies show that example-based tools for preference-based search can achieve significantly higher accuracy when they are complemented with suggestions chosen to inform users about the available choices.
We discuss the problem of eliciting preferences in example-based tools and present the lookahead principle for generating suggestions. We compare two different implementations of this principle and we analyze logs of real user interactions to evaluate them.
Keywords
- Dominance Relation
- Preference Model
- Pareto Optimality
- Skyline Query
- Preference Elicitation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Viappiani, P., Faltings, B., Pu, P. (2006). The Lookahead Principle for Preference Elicitation: Experimental Results. In: Larsen, H.L., Pasi, G., Ortiz-Arroyo, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2006. Lecture Notes in Computer Science(), vol 4027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766254_32
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DOI: https://doi.org/10.1007/11766254_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34638-8
Online ISBN: 978-3-540-34639-5
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