Walk the line: Toward an efficient user model for recommendations in museums

Pierre-Edouard Osche 1 Sylvain Castagnos 1 Amedeo Napoli 2 Yannick Naudet 3
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
2 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Contrary to many application domains, recommending items within a museum is not only a question of preferences. Of course, the visitors expect suggestions that are likely to interest or please them. However, additional factors should be taken into account. Recent works use the visiting styles or the shortest distance between items to adapt the list of recommendations. But, as far as we know, no model of the literature aims at inferring in real time a holistic user model which includes variables such as the crowd tolerance, the distance tolerance, the expected user control, the fatigue, the congestion points, etc. As a work-in-progress, we propose a new representation model which includes psychological, physical and social variables so as to increase user satisfaction and enjoyment. We show how we can infer these characteristics from the user observations (geolocalization over time, moving speed,. . .) and we discuss how we can use them jointly for a sequence recommendation purpose. This work is still in an early stage of development and remains more theoretical than experimental.
Type de document :
Communication dans un congrès
11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2016), Oct 2016, Thessaloniki, Greece. pp.83 - 88, 2016, 〈10.1109/SMAP.2016.7753389〉
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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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Pierre-Edouard Osche, Sylvain Castagnos, Amedeo Napoli, Yannick Naudet. Walk the line: Toward an efficient user model for recommendations in museums. 11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2016), Oct 2016, Thessaloniki, Greece. pp.83 - 88, 2016, 〈10.1109/SMAP.2016.7753389〉. 〈hal-01421548〉

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