Understanding Usages by Modeling Diversity over Time

Amaury L'Huillier 1 Sylvain Castagnos 1 Anne Boyer 1
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Let's imagine a system that can recommend the kind of mu-sic (among other application domains) you like to listen when you are at work, without having to know your location, IP address or even to ask your current mood. In this paper, we bring this dream closer by proposing a model that can automatically understand the user's current context. This model, called DANCE, analyzes the attributes of the items in your recent history and monitors the relative diversity brought by your consultations over time. We validated our approach with a music corpus of 100 users and a global history of 204,758 plays.
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Communication dans un congrès
22nd Conference on User Modeling, Adaptation, and Personalization, Jul 2014, Aalborg, Denmark. 1181, 2014, UMAP 2014 Extended Proceedings
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Contributeur : Sylvain Castagnos <>
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Dernière modification le : jeudi 11 janvier 2018 - 06:25:24
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Amaury L'Huillier, Sylvain Castagnos, Anne Boyer. Understanding Usages by Modeling Diversity over Time. 22nd Conference on User Modeling, Adaptation, and Personalization, Jul 2014, Aalborg, Denmark. 1181, 2014, UMAP 2014 Extended Proceedings. 〈hal-01108990〉

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