Walk the Line: Toward an Efficient User Model for Recommendations in Museums

dc.contributor.author	Osche, P.-E	en
dc.contributor.author	Castagnos, Sylvain	en
dc.contributor.author	Napoli, A.	el
dc.contributor.author	Naudet, Yannick	en
dc.date.accessioned	2019-04-07T07:59:25Z
dc.date.available	2019-04-07T07:59:25Z
dc.date.issued	2019-04-07
dc.identifier.uri	http://hdl.handle.net/11400/20262
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.title	Walk the Line: Toward an Efficient User Model for Recommendations in Museums	en
heal.type	conferenceItem
heal.language	en
heal.access	free
heal.publicationDate	2016
heal.bibliographicCitation	Osche, P.-E., Castagnos, S., Napoli, A. and Naudet, Y. (2016), "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), October 20-21, 2016, Thessaloniki, Greece	en
heal.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 an 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.	en
heal.sponsor	This publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 693150	en
heal.fullTextAvailability	false
heal.conferenceName	11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2016), October 20-21, 2016, Thessaloniki, Greece	en
heal.conferenceItemType	full paper