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Analysis of strategies for building group profiles

Christophe Senot 1 Kostadinov Dimitre 1 Makram Bouzid 1 Jérome Picault 1 Armen Aghasaryan 1 Cédric Bernier 1, 2 
2 KIWI - Knowledge Information and Web Intelligence
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Today most of existing personalization systems (e.g. content recommenders, or targeted ad) focus on individual users and ignore the social situation in which the services are consumed. However, many human activities are social and involve several individuals whose tastes and expectations must be taken into account by the service providers. When a group profile is not available, different profile aggregation strategies can be applied to recommend adequate content and services to a group of users based on their individual profiles. In this paper, we consider an approach intended to determine the factors that influence the choice of an aggregation strategy. We present a preliminary evaluation made on a real large-scale dataset of TV viewings, showing how group interests can be predicted by combining individual user profiles through an appropriate strategy. The conducted experiments compare the group profiles obtained by aggregating individual user profiles according to various strategies to the "reference" group profile obtained by directly analyzing group consumptions.
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Submitted on : Monday, December 20, 2010 - 5:50:50 PM
Last modification on : Friday, February 26, 2021 - 3:28:08 PM


  • HAL Id : inria-00548890, version 1



Christophe Senot, Kostadinov Dimitre, Makram Bouzid, Jérome Picault, Armen Aghasaryan, et al.. Analysis of strategies for building group profiles. 18th International Conference on User Modeling, Adaptation and Personalization - UMAP 2010, Jun 2010, Big Island, HI, United States. pp.40--51. ⟨inria-00548890⟩



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