Comparative preferences induction methods for conversational recommenders

Research output: Chapter in Book/Report/Conference proceedingsChapterpeer-review

Abstract

In an era of overwhelming choices, recommender systems aim at recommending the most suitable items to the user. Preference handling is one of the core issues in the design of recommender systems and so it is important for them to catch and model the user's preferences as accurately as possible. In previous work, comparative preferences-based patterns were developed to handle preferences deduced by the system. These patterns assume there are only two values for each feature. However, real-world features can be multi-valued. In this paper, we develop preference induction methods which aim at capturing several preference nuances from the user feedback when features have more than two values. We prove the efficiency of the proposed methods through an experimental study.

Original languageEnglish
Title of host publicationAlgorithmic Decision Theory - Third International Conference, ADT 2013, Proceedings
PublisherSpringer Verlag
Pages363-374
Number of pages12
ISBN (Print)9783642415746
DOIs
Publication statusPublished - 2013
Event3rd International Conference on Algorithmic Decision Theory, ADT 2013 - Bruxelles, Belgium
Duration: 13 Nov 201315 Nov 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8176 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Algorithmic Decision Theory, ADT 2013
Country/TerritoryBelgium
CityBruxelles
Period13/11/1315/11/13

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