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Conference papers

Adaptive preference elicitation for top-k recommendation tasks using GAI-networks

Sergio Queiroz 1 
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : The enormous number of questions needed to acquire a full preference model when the size of the outcome space is large forces us to work with partial models that approximate the user's preferences. In this way we must devise elicitation strategies that focus on the most important questions and at the same time do not need to enumerate the outcome space. In this paper we focus on adaptive elicitation of GAI-decomposable preferences for top-k recommendation tasks in large combinatorial domains. We propose a method that interleaves the generation of top-k solutions with a heuristic selection of questions for refining the user preference model. Empirical results for a large combinatorial problem are given.
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Conference papers
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Submitted on : Wednesday, May 4, 2016 - 4:07:32 PM
Last modification on : Sunday, June 26, 2022 - 10:04:08 AM


  • HAL Id : hal-01311758, version 1


Sergio Queiroz. Adaptive preference elicitation for top-k recommendation tasks using GAI-networks. The 25th IASTED International Multi-Conference Artificial Intelligence and Applications, Feb 2007, Innsbruck, Austria. pp.579-584. ⟨hal-01311758⟩



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