Interaction Design in a Mobile Food Recommender System
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
CEUR-WSDate
2015-09-19Citation
IntRS 2015 Interfaces and Human Decision Making for Recommender Systems: Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2015). Ed. John O'Donovan, Alexander Felfernig, Nava Tintarev, Peter Brusilovsky, Giovanni Semeraro, Pasquale Lops. CEUR Workshop Proceedings, Volume 1438, 2015. 49-52ISSN
1613-0073Editor's Version
http://ceur-ws.org/Vol-1438/Subjects
Benchmarking; Decision making; Design; User interfaces In-buildings; Interaction design; Personalized recommendation; Short term; User experience; User study; Recommender systems; InformáticaNote
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Rights
© 2015 The author/sAbstract
One of the most important steps in building a recommender
system is the interaction design process, which de nes how
the recommender system interacts with a user. It also shapes
the experience the user gets, from the point she registers
and provides her preferences to the system, to the point
she receives recommendations generated by the system. A
proper interaction design may improve user experience and
hence may result in higher usability of the system, as well
as, in higher satisfaction.
In this paper, we focus on the interaction design of a mo-
bile food recommender system that, through a novel interac-
tion process, elicits users' long-term and short-term prefer-
ences for recipes. User's long-term preferences are captured
by asking the user to rate and tag familiar recipes, while for
collecting the short-term preferences, the user is asked to
select the ingredients she would like to include in the recipe
to be prepared. Based on the combined exploitation of both
types of preferences, a set of personalized recommendations
is generated. We conducted a user study measuring the us-
ability of the proposed interaction. The results of the study
show that the majority of users rates the quality of the rec-
ommendations high and the system achieves usability scores
above the standard benchmark.
Files in this item
Google Scholar:Elahi, Mehdi
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Ge, Mouzhi
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Ricci, Francesco
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Fernández-Tobías, Ignacio
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Berkovsky, Shlomo
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David, Massimo
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